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The ApolloScape Dataset for Autonomous Driving

by   Xinyu Huang, et al.

Scene parsing aims to assign a class (semantic) label for each pixel in an image. It is a comprehensive analysis of an image. Given the rise of autonomous driving, pixel-accurate environmental perception is expected to be a key enabling technical piece. However, providing a large scale dataset for the design and evaluation of scene parsing algorithms, in particular for outdoor scenes, has been difficult. The per-pixel labelling process is prohibitively expensive, limiting the scale of existing ones. In this paper, we present a large-scale open dataset, ApolloScape, that consists of RGB videos and corresponding dense 3D point clouds. Comparing with existing datasets, our dataset has the following unique properties. The first is its scale, our initial release contains over 140K images - each with its per-pixel semantic mask, up to 1M is scheduled. The second is its complexity. Captured in various traffic conditions, the number of moving objects averages from tens to over one hundred. And the third is the 3D attribute, each image is tagged with high-accuracy pose information at cm accuracy and the static background point cloud has mm relative accuracy. We are able to label these many images by an interactive and efficient labelling pipeline that utilizes the high-quality 3D point cloud. Moreover, our dataset also contains different lane markings based on the lane colors and styles. We expect our new dataset can deeply benefit various autonomous driving related applications that include but not limited to 2D/3D scene understanding, localization, transfer learning, and driving simulation.


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1 Introduction

Asuccessful self-driving vehicle that is widely applied must include three essential components. Firstly, understanding the environment, where commonly a 3D semantic HD map at the back-end precisely recorded the environment. Secondly, understanding self-location, where an on-the-fly self-localization system puts the vehicles accurately inside the 3D world, so that it can plot a path to every target location. Thirdly, understanding semantics in the view, where a 3D perceptual system detects other moving objects, guidance signs and obstacles on the road, in order to avoid collisions and perform correct actions. The prevailing approaches for solving those tasks from self-driving companies are mostly dependent on LIDAR [4], whereas vision-based approaches, which have potentially very low-cost, are still very challenging and under research. It requires solving tasks such as learning to do visual 3D scene reconstruction [5, 6, 7, 8], self-localization [9, 10], semantic parsing [11, 12], semantic instance understanding [13, 14], object 3D instance understanding [15, 16, 17, 18] online in a self-driving video etc. However, the SOTA datasets for supporting these tasks either have limited amount, e.g.KITTI [2] only has 200 training images for semantic understanding, or limited variation of tasks, e.g.Cityscapes [3] only has discrete semantic labelled frames without tasks like localization or 3D reconstruction. Therefore, in order to have a holistic training and evaluation of a vision-based self-driving system, in this paper, we build Apolloscape [1] for autonomous driving, which is a growing and unified dataset extending previous ones both on the data scale, label density and variation of tasks.

Fig. 1: A glance of ApolloScape with various properties. The images are cropped for better visualization. At bottom, we show the road map summarizing the past and future of ApolloScape.

Specifically, in current stage, ApolloScape contains properties of,

  1. dense semantics 3D point cloud for the environment (20+ driving site)

  2. stereo driving videos (100+ hours)

  3. high accurate 6DoF camera pose. (translation mm, rotation )

  4. videos at same site under different day times, (morning, noon, night)

  5. dense per-pixel per-frame semantic labelling (35 classes, 144K+ images)

  6. per-pixel lanemark labelling (35 classes, 160K+ images)

  7. semantic 2D instances segmentation (8 classes, 90K+ images)

  8. 2D car keypoints and 3D car instance labelling (70K cars)

With these information, we have released several standard benchmarks for scene parsing [19], instance segmentation [20], lanemark parsing [21], self-localization [22] by withholding part of the data as test set, and our toolkit for visualization and evaluation has also published [23]. Here, for 3D car instance, we list the car number we already labelled, and since it is still under development, we will elaborate it in our future work. Fig. 1 shows a glance of ApolloScape, which illustrates various information from the dataset that is necessary for autonomous driving. More excitingly, we show a road map of ApolloScape at the bottom of Fig. 1

. Our dataset is still growing and evolving, and will shortly contains new tasks such as 3D car instance shape and pose, 3D car tracking etc., which are important for scene understanding in fine granularity. In addition, thanks to our efficient labelling pipeline, we are able to scale the dataset to multiple cities and sites, and we plan to contain at least 10 cities in China early next year under various weather conditions such as raining, snowing and foggy.

Based on ApolloScape dataset, we are able to develop algorithms for jointly considering 3D and 2D simultaneously with multiple tasks like segmentation, reconstruction, self-localization etc. These tasks are traditionally handled individually [9, 12], or jointly handled offline with semantic SLAM [24]

which can be time consuming. However, from a more practical standpoint, self-driving car needs to handle localization and parsing the environment on-the-fly efficiently. Therefore, in this paper, we also propose a deep learning based online algorithm jointly solving localization and semantic scene parsing when a 3D semantic map is available. In our system, we assume to have (a) GPS/IMU signal to provide a coarse camera pose estimation; (b) a semantic 3D map for the static environment. The GPS/IMU signals serve as a crucial prior for our pose estimation system. The semantic 3D map, which can synthesize a semantic view for a given camera pose, not only provides strong guidance for scene parsing, but also helps maintain temporal consistency.

With our framework, the camera poses and scene semantics are mutually beneficial. The camera poses help establish the correspondences between the 3D semantic map and 2D semantic label map. Conversely, scene semantics could help refine camera poses. Our unified framework yields better results, in terms of both accuracy and speed, for both tasks than doing them individually. In our experiments, using a single Titan Z GPU, the networks in our system estimates the pose in 10ms with accuracy under 1 degree, and segments the image within 90ms with pixel accuracy around 96 without model compression, which demonstrates its efficiency and effectiveness.

In summary, the contributions of this work are in three folds,

  1. We build a large and rich dataset, named as ApolloScape, which includes various tasks, e.g.3D reconstruction, self-localization, semantic parsing, instance segmentation etc., supporting the training and evaluation of vision-based autonomous driving algorithms and systems.

  2. For developing the dataset, an efficient and scalable 2D/3D joint-labelling pipeline is designed, where various tools are developed for 2D segmentation, 3D instance understanding etc. For example, compare to fully manual labelling, our 3D/2D labelling pipeline saves 70% semantic labeling time over the fully manual labelling.

  3. Based on our dataset, we developed a deep learning based self-localization and segmentation algorithms, which is relying on a semantic 3D map. The system fuses sensors from camera and customer-grad GPS/IMU, which runs efficiently and improves the robustness and accuracy for camera localization and scene parsing.

The structure of this paper is organized as follows. We provide related work in Sec. 2, and elaborate the collection and labelling of ApolloScape in Sec. 3. In Sec. 4, we explain the developed efficient joint segmentation and localization algorithm. Finally, we present the evaluation results of our algorithms, the benchmarks for multiple tasks and corresponding baseline algorithms performed on these tasks in Sec. 5.

2 Related works

Autonomous driving datasets and related algorithms has been an active research area for years. Here we summarize the related works in aspects of datasets and most relevant algorithms without enumerating them all due to space limitation.

Dataset Real Location Accuracy Diversity Annotation
3D 2D Video Lane
CamVid [25] - day time no pixel: 701 2D / 2 classes
Kitti [2] cm day time 80k 3D box box: 15k - no
pixel: 400
Cityscapes [3] - day time no pixel: 25k - no
50 cities
Toronto [26] cm Toronto focus on buildings and roads
exact numbers are not available1
Mapillary [27] meter various weather no pixel: 25k - 2D / 2 classes
day & night
6 continents
BDD100K [28] meter various weather no box: 100k - 2D / 2 classes
day pixel: 10k
4 regions in US
SYNTHIA [29] - - various weather box pixel:213k no
P.F.B. [30] - - various weather box pixel:250k no
ApolloScape cm various weather 3D semantic point pixel: 140k 3D / 2D Video
day time 70K 3D fitted cars 35 classes
4 regions in China
  • database is not open to public yet.

TABLE I: Comparison between our dataset and the other street-view self-driving datasets published. “pixel” represents 2D pixel-level annotations. “point” represents 3D point-level annotations. “box” represents bounding box-level annotations. “Video” indicates whether 2D video sequences are annotated. “3D fitted cars” gives the number of car instance we already fitted in the images with a 3D mesh model, which we will introduce in our future works.

2.1 Datasets for autonomous driving.

Most recently, various datasets targeting at solving each individual visual task for robot navigation have been released such as 3D geometry estimation [31, 32], localization [9, 33], instance detection and segmentation [34, 35]. However, focusing on autonomous driving, a set of comprehensive visual tasks are preferred to be collected consistently within a unified dataset from driving videos, so that one may explore the mutual benefits between different problems.

In past years, lots of datasets have been collected in various cities, aiming to increase variability and complexity of urban street views for self-driving applications. The Cambridge-driving Labeled Video database (CamVid) [25] is the first dataset with semantic annotated videos. The size of the dataset is small, containing 701 manually annotated images with 32 semantic classes. The KITTI vision benchmark suite [2] is later collected and contains multiple computer vision tasks such as stereo, optical flow, 2D/3D object detection and tracking. For semantics, it mainly focuses on detection, where 7,481 training and 7,518 test images are annotated by 2D and 3D bounding boxes, and each image contains up to 15 cars and 30 pedestrians. Nevertheless, for segmentation, very few images contain pixel-level annotations, yielding a relatively weak benchmark for semantic segmentation. Most recently, the Cityscapes dataset [3] is specially collected for 2D segmentation which contains 30 semantic classes. In detail, 5,000 images have detailed annotations, and 20,000 images have coarse annotations. Although video frames are available, only one image out of each video is manually labelled. Thus, tasks such as video segmentation can not be performed. Similarly, the Mapillary Vistas dataset [27] provides a larger set of images with fine annotations, which has 25,000 images with 66 object categories. The TorontoCity benchmark [26] collects LIDAR data and images including stereo and panoramas from both drones and moving vehicles. Although the dataset scale is large, which covers the Toronto area. as mentioned by authors, it is not possible to manually do per-pixel labelling of each frame. Therefore, only two semantic classes, i.e., building footprints and roads, are provided for benchmarks of segmentation. BDD100K database [28] contains 100K raw video sequences representing more than 1000 hours of driving hours with more than 100 million images. Similarly with the Cityscapes, one image is selected from each video clip for annotation. 100K images are annotated in bounding box level and 10K images are annotated in pixel level.

Real data collection is laborious, to avoid the difficulties in real scene collection, several synthetic datasets are also proposes. SYNTHIA [29] builds a virtual city with Unity development platform [36], and Play for benchmark [30] extracts ground truth with GTA game engine. Though large amount of data and ground truth can be generated, there is still a domain gap [37] between appearance of synthesized images and the real ones. In general, models learned in real scenario still generalize better in real applications such as object detection and segmentation [38, 39].

In Tab. I, we compare the properties our dataset and other SOTA datasets for autonomous driving, and show that ApolloScape is unique in terms of data scale, granularity of labelling, task variations within real environments. Later in Sec. 3, we will present more details about the dataset.

2.2 Self-localization and semantic scene parsing.

As discussed in Sec. 1, we also try to tackle real-time self-localization and semantic scene parsing back on ApolloScape given a video or a single image. These two problems have long been center focus for computer vision. Here we summarize the related works on outdoor cases with street-view images as input.

Visual self-localization. Traditionally, localizing an image given a set of 3D points is formulated as a Perspective--Point (PP) problem [40, 41] by matching feature points in 2D and features in 3D through cardinality maximization. Usually in a large environment, a pose prior is required in order to obtain good estimation [42, 43]. Campbell et al. [44] propose a global-optimal solver which leverage the prior. In the case that geo-tagged images are available, Sattler et al. [45] propose to use image-retrieval to avoid matching large-scale point cloud. When given a video, temporal information could be further modeled with methods like SLAM [46] etc, which increases the localization accuracy and speed.

Although these methods are effective in cases with distinguished feature points, they are still not practical for city-scale environment with billions of points, and they may also fail in areas with low texture, repeated structures, and occlusions. Thus, recently, deep learned features with hierarchical representations are proposed for localization. PoseNet [9, 47] takes a low-resolution image as input, which can estimate pose in 10ms w.r.t. a feature rich environment composed of distinguished landmarks. LSTM-PoseNet [48] further captures a global spatial context after CNN features. Given an video, later works incorporate Bi-Directional LSTM [49]

or Kalman filter LSTM 

[50] to obtain better results with temporal information. Most recently, many works [10, 51] also consider adding semantic cues as more robust representation for localization. However, in street-view scenario, considering a road with trees aside, in most cases, no significant landmark appears, which could fail the visual models. Thus, signals from GPS/IMU are a must-have for robust localization in these cases [52], whereas the problem switched to estimating the relative pose between the camera view from a noisy pose and the real pose. For finding relative camera pose of two views, recently, researchers [53, 54] propose to stack the two images as a network input. In our case, we concatenate the real image with an online rendered label map from the noisy pose, which provides superior results in our experiments.

Street scene parsing. For parsing a single image of street views (e.g., these from CityScapes [3]), most state-of-the-arts (SOTA) algorithms are designed based on a FCN [11] and a multi-scale context module with dilated convolution [12], pooling [55], CRF [56], or spatial RNN [57]. However, they are dependent on a ResNet [58] with hundreds of layers, which is too computationally expensive for applications that require real-time performance. Some researchers apply small models [59] or model compression [60] for acceleration, with the cost of reduced accuracy. When the input is a video, spatial-temporal informations are jointly considered, Kundu et al. [61] use 3D dense CRF to get temporally consistent results. Recently, optical flow [62] between consecutive frames is computed to transfer label or features [63, 64] from the previous frame to current one. In our case, we connect consecutive video frames through 3D information and camera poses, which is a more compact representation for static background. In our case, we propose the projection from 3D maps as an additional input, which alleviates the difficulty of scene parsing solely from image cues. Additionally, we adopt a light weighted network from DeMoN [54] for inference efficiency.

Joint 2D-3D for video parsing. Our work is also related to joint reconstruction, pose estimation and parsing [24, 65] through embedding 2D-3D consistency. Traditionally, reliant on structure-from-motion (SFM) [65] from feature or photometric matching, those methods first reconstruct a 3D map, and then perform semantic parsing over 2D and 3D jointly, yielding geometrically consistent segmentation between multiple frames. Most recently, CNN-SLAM [66] replaces traditional 3D reconstruction module with a single image depth network, and adopts a segment network for image parsing. However, all these approaches are processed off-line and only for static background, which do not satisfy our online setting. Moreover, the quality of a reconstructed 3D model is not comparable with the one collected with a 3D scanner.

3 Build ApolloScape

In this section, we introduce our acquisition system, specifications about the collected data and efficient labelling process for building ApolloScape.

3.1 Acquisition system

In Fig. 2, we visualize our collection system. To collect static 3D environment, we adopt Riegl VMX-1HA [67] as our acquisition system that consists of two VUX-1HA laser scanners ( FOV, range from 1.2m up to 420m with target reflectivity larger than 80%), one VMX-CS6 camera system (two front cameras are used with resolution ), and a measuring head with IMU/GNSS (position accuracy mm, roll & pitch accuracy , and heading accuracy ). The laser scanners utilizes two laser beams to scan its surroundings vertically that are similar to the push-broom cameras. Comparing with common-used Velodyne HDL-64E [4], the scanners are able to acquire higher density of point clouds and obtain higher measuring accuracy / precision (5mm / 3mm). The whole system has been internally calibrated and synchronized, and is mounted on the top of a mid-size SUV.

Additionally, the system contains two high frontal camera capturing with a resolution of , and is well calibrated with the LIDAR device. Finally, for obtain high accurate GPS/IMU information, a temporary GPS basement is set up near the collection site to make sure the localization of the camera is sufficiently accurate for us match the 2D image and 3D point cloud. Commonly, our vehicle drives at the speed of 30km per hour and the cameras are triggered once every meter, i.e.30fps.

Fig. 2: Acquisition system consists of two laser scanners, up to six video cameras, and a combined IMU/GNSS system.

3.2 Specifications

Here, based on the acquisition system, we first present the specifications of Apolloscape w.r.t. different tasks, e.g.predefined semantic classes, lanemark classes and instance etc., to allow better overview of the dataset. In Sec. 3.3, we will introduce our active labelling pipeline which allows us to efficiently produce the ground truth of multiple tasks simultaneously.

Semantic scene parsing. In our current version released online [19, 20], we have 143,906 video frames and their corresponding pixel-level semantic labelling, from which 89,430 images contain instance-level annotations where movable objects are further separated. Notice that our labelled images contains temporal information which could also be useful for video semantic and object segmentation.

To make the evaluation more comprehensive, similar to the Cityscapes [3], we also separate the images with the level of easy, moderate, and heavy scene complexities based on the amount of movable objects in an image, such as person and vehicles. Tab. II compares the scene complexities between Apolloscape, the Cityscapes [3] and KITTI [2], where we show the statistics for each individual classes of movable objects. We find Apolloscape contains much more objects than others in terms of both total number and average number of object instances from images. More importantly, our dataset contains lots of challenging environments, as shown in Fig. 3. For instance, high contrast regions due to sun light and large area of shadows from the overpass. Mirror reflections of multiple nearby vehicles on a bus glass due to highly crowded transportation. We hope these case can help and motivate researchers to develop more robust models against environment changes.

Count Kitti Cityscapes BDD100K ApolloScape
(box) (pixel) (box) (pixel)
person 0.6 2.4 12.9 54.3
vehicle 3.0 4.1 110.2 198.9
average per image e m h
person 0.8 7.0 1.3 1.1 6.2 16.9
vehicle 4.1 11.8 11.0 12.7 24.0 38.1
car - - 10.2 9.7 16.6 24.5
motorcycle - - 0.0 0.1 0.8 2.5
bicycle - - 0.1 0.2 1.1 2.4
rider - - 0.1 0.8 3.3 6.3
truck - - 0.4 0.8 0.8 1.4
bus - - 0.2 0.7 1.3 0.9
tricycle 0.0 0.0 0.0 0.4 0.3 0.2
train - - 0.0 0.0 0.0 0.0
TABLE II: Total and average number of instances in KITTI [2], Cityscapes [3], BDD100K [28], and our dataset (instance-level). “pixel” represents 2D pixel-level annotations. “box” represents bounding box-level annotations. The letters, e, m, and h, indicate easy, moderate, and hard subsets respectively.
Fig. 3: Some examples with challenging environments for object detection and segmentation (Images are center-cropped for better visualization). We highlight and zoom in the region of challenges in each image. (a) Objects with heavy occlusion and small scales. (b) Abnormal action by cyclist drivers. (c) High contrast and overexposure due to shadows and strong sun-lights. (d) Mirror reflection on bus glasses.

For semantic scene parsing, we annotate 25 different labels five groups. Tab. III gives the details of these labels. The IDs shown in the table are the IDs used for training. The value 255 indicates the ignoring labels that currently are not evaluated during the testing phase. The specifications of the classes are partially borrowed from the Cityscape dataset by adding several additional classes. For instance, we add one new “tricycle” class that is one of the most popular means of transportation in China. This class covers all kinds of three-wheeled vehicles that could be both motorized and human-powered. The rider class in the Cityscape is defined as the person on means of transportation. Here, we also consider the person and the means of transportation as a single moving object, and treat the two together as a single class. The three classes related to rider, i.e., bicycle, motorcycle, and tricycle, represent means of transportation without rider and parked along the roads.

Group Class ID Description
movable car 1
object motorcycle 2
bicycle 3
person 4
rider 5 person on
bicycle or
truck 6
bus 7
tricycle 8 three-wheeled
motorized, or
surface road 9
sidewalk 10
infrastructure traffic cone 11 movable and
cone-shaped markers
bollard 12 fixed with many
different shapes
fence 13
traffic light 14
pole 15
traffic sign 16
wall 17
trash can 18
billboard 19
building 20
bridge 255
tunnel 255
overpass 255
nature vegetation 21
void void 255 other unlabeled
TABLE III: Details of semantic classes in our dataset.

Semantic lanemark segmentation. Automatically understanding lane mark is perhaps the most important function for autonomous driving since it is the guidance for possible actions. In Apolloscape, 35 different lane markings are labelled as elaborated in Tab. IV. The labels are defined based on lane mark attributes including color (e.g., white and yellow) and type (e.g., solid and broken). To be specific, 165949 images from 3 road sites are labelled and released online [21], where 33760 images are withheld for testing. Comparing to other public available datasets such as KITTI [2] or the one from Tusimple [68], Apolloscape is the first large dataset containing rich semantic labelling for lane marks with many variations.

Type Color Use ID
solid w dividing 200
solid y dividing 204
double solid w dividing, no pass 213
double solid y dividing, no pass 209
solid & broken y dividing, one-way pass 207
solid & broken w dividing, one-way pass 206
broken w guiding 201
broken y guiding 203
double broken y guiding 208
double broken w guiding 211
double broken w stopping 216
double solid w stopping 217
solid w stopping 215
solid w chevron 218
solid y chevron 219
solid w parking 210
solid n/a parking 232
crosswalk w zebra 214
arrow w u-turn 202
arrow w thru 220
arrow w thru & left turn 221
arrow w thru & right turn 222
arrow w thru & left & right turn 231
arrow w left turn 224
arrow w right turn 225
arrow w left & right turn 226
arrow w left & u-turn 230
arrow w thru & u-turn 228
arrow w merge 229
arrow y thru 233
bump n/a speed reduction 205
diamond w/y zebra attention 212
rectangle w/y no parking 227
visible old marking y/w n/a 223
other markings n/a n/a 250
void void other unlabeled 255
TABLE IV: Details of lane mark labels in our dataset (y: yellow, w:white).

Self-localization. Each frame of our recorded video is tagged with high accurate GPS/IMU signal automatically. Therefore, the dataset we released for segmentation are also available for self-localization research. However, in order to setup a benchmark for this task, we need a much larger set of images, and withhold partial data for evaluation, which was not considered when doing semantic labelling. Therefore, most recently, we prepare another large set of self-localization dataset [22] from 7 roads at 4 different cities, which contains roughly 300k images, and road of 28.

Our recently released self-localization dataset has variations under different lighting, i.e.morning, noon and night, and driving conditions, i.e.rush and non-rush hours, with stereo pair of images available. In addition, each road has a point cloud based 3D map that can be used for 3D feature learning etc. Finally, we record each road by driving from start-to-end and then end-to-start, which means each position along a road will be looked at from two opposite directions. Therefore, our dataset also supports research of localization with large view changes such as that proposed in semantic visual localization [10].

Later, we will have all our point cloud semantically labelled, and then record additional test sequences on the sites we have already collected for segmentation in order to support learning and fusion of multitask models.

3.3 Labeling Process

In order to make our labeling of video frames accurate and efficient, we develop an active labelling pipeline by jointly consider 2D and 3D information, as shown in Fig. 4. The pipeline mainly consists of two stages, 3D labeling and 2D labeling, to handle static background/objects and moving objects respectively. The basic idea of our pipeline is similar to the one described in [69], which transfers the 3D labelled results to 2D images by camera projection, while we handle much larger amount of data and have different set up of the acquisition vehicle. Thus some key techniques used in our pipeline are re-designed, which we will elaborate later.

Fig. 4: Our 2D/3D labeling pipeline that label static background/objects and moving objects separately. We also adopt active strategies for accelerating the labelling process for scalability of the labelling process. For inputs, since GPS/IMU still has some errors, we manually add control points to better align the point clouds and our image frames. In Sec. 3.3, we present the details of each components.

Moving object removal. As mentioned in Sec. 3.1, LIDAR scanner is accurate in static background, while due to low scanning rate, the point clouds of moving objects, such as vehicles and pedestrians running on the road, could be compressed, expanded, or completely missing in the captured point clouds as illustrated in Fig. 6(b). Thus, we design to handle labelling static background and moving object separately, as shown in Fig. 4. Specifically, in the first step, we do moving object removal from our collected point clouds by 1) scan the same road segment multiple rounds; 2) align these point clouds based on manually selected control points; 3) remove the points based on the temporal consistency. Formally, the condition to kept a point in round is,


where and in our setting, and is an indicator function. It indicates that a 3D point will be kept if it appears with high frequency in many rounds of recording, i.e.60 of all times. We keep the remained point clouds as a static background for semantic labelling.

3D labelling. Next, for labelling static background (3D Labeling), rather than label each 3D point and loading all the points, we first separate the 3D points into multiple parts, and over-segment each part of point clouds into point clusters based on spatial distances and normal directions using locally convex connected patches (LCCP) [70] implemented with PCL [71]. Then, we label these point clusters manually using our in-house developed 3D labelling tool as shown in Fig. 5, which can easily do point cloud rotation, (inverse-)selection by polygons, matching between point clouds and camera views, etc.. Notice at this stage, there will be point clouds belonging to movable but static objects such as bicycles and cars parking aside the road. These point clouds are remained in our background, and also labelled in 3D which are valuable to increase our label efficiency of objects in 2D images.

To further improve 3D point cloud labelling efficiency, after labelling of one road, we actively train a PointNet++ model [72] to pre-label the over-segmented point cloud clusters of the next road. Labellers are then asked to refine and correct the results by fixing wrong annotations, which often occur near the object boundaries. With the growing number of labelled point clouds, our learned model can label new roads with increasing accuracy, yielding accelerated labelling process, which scales up to various cities and roads.

Fig. 5: The user interface of our 3D labeling tool. At left-top, we show the pre-defined color code of different classes. At left-bottom, we show the labelling logs which can be used to revert the labelling when mistakes happen. At center part, labelled point cloud is shown indicating the labelling progress.
Fig. 6: A labelled example of the labelling pipeline for semantic parsing, a subset of color coded labels are shown below. (a) Image. (b) Rendered label map with 3D point cloud projection, with an inaccurate moving object (rider) circled in blue. (c) Rendered label map with 3D point cloud projection after points with low temporal consistency being removed. (d) (e) Rendered depth map of background and rendered label map after class dependent splatting in 3D point clouds (Sec. 3.3). (f) Merged label map with missing region in-painted, moving objects and sky. (g) Another label map with very small traffic lights. Details are zoomed out highlighting the details of our rendered label maps. Other examples of our labeled videos is shown online [19].

Splatting Projection. Once the 3D annotations are generated, the annotations of static background/objects for all the 2D image frames are generated automatically by 3D-2D projections. In our setting, the 3D map is a point cloud based environment. Although the density of the point cloud is very high (one point per 25mm within road regions), when the 3D points are far away from the camera, the projected labels could be sparse, e.g.regions of buildings shown in Fig. 6(c). Thus for each point in the environment, we adopt the point splatting technique, by enlarging the 3D point to a square where the square size is determined by its semantic class.

Formally, given a 6-DOF camera pose , where is the quaternion representation of rotation and is translation, a label map can be rendered from the semantic 3D map, where z-buffer is applied to find the closest point at each pixel. For a 3D point belonging a class , its square size is set to be proportional to the class’ average distance to the camera. Formally,


where is the set of 3D points belong to class , and is the set of ground truth camera poses. Then, given the relative square size between different classes, we define an absolute range to obtain the actual square size for splatting. This is non-trivial since too large size will result in dilated edges, while too small size will yield many holes. In our experiments, we set the range as , and find that it provides the highest visual quality. As shown in Fig. 6(e), invalid values in-between those projected points are well in-painted, meanwhile the boundaries separating different semantic classes are also well preserved, yielding the both the background depth map and 2D labelled background. With such a strategy, we increase labelling efficiency and accuracy for video frames. For example, it could be very labor-intensive to label texture-rich regions like trees, poles and traffic lights further away, especially when occlusion happens like fence on the road as illustrated in Fig. 6(g).

2D labelling of objects. Finally, to generate the final labels (Fig. 6(f)), we need to label the moving objects in the environments, and fix missing parts at background like sky regions. Similar with 3D point cloud labelling, we also developed an in-house 2D labelling tool with the same interface as 3D tool in Fig. 5. To speed up the 2D semantic labeling, we also use a labelling strategy by training a CNN network for movable objects and background [73] to pre-segment the 2D images. For segmenting background, we test with original image resolution collected by our camera, where the resolution is much higher than that used in the original paper to increase the quality of predicted region boundaries. For segment objects, similar with MaskRCNN [13], we first do 2D object detection with faster RCNN [74], and segment object masks inside. However, since we consider high requirements for object boundaries rather than class accurate, for each bounding box with high confidence (), we enlarge the bounding box and crop out the object region with context similar to [75]. Then, we upsample the cropped image to a higher resolution by setting a minimum resolution of prediction (minimum len greater than ), and segment out the mask with an actively trained mask CNN network with the same architecture in [73]. The two networks for segmenting background and objects are updated when images in one road is labelled.

Finally, the segmented results from the networks are fused with our rendered label map from the semantic 3D point clouds following two rules: 1) for fuse segmented label map from the background network, we fill the predicted label in the pixels without 3D projection, yielding a background semantic map. 2) for fuse semantic object label segmented by object network, we pasted the object mask over the fused background map, without replacing the projected static movable object mask rendered from 3D points as mentioned in 3D labelling. We provided this fused label map for labellers to further fine tuning when error happens especially around object boundary or occlusion from the object masks. In addition, the user can omit any of the pre-segmented results from CNNs to do relabelling if the segmented results are far from satisfaction. Our label tool supports multiple actions such as polygons and pasting brushes etc., which are commonly adopted by many popular open source label tools 111

A final labelled example is shown in Fig. 6(f)

(g). Notice that some background classes such as fence, traffic light, and vegetation are annotated in details using our projection and missing parts such as building glass can be fill in. Thanks to 3D and active learning, our overall pipeline save us significant efforts in dense per-pixel and per-frame semantic labelling for background and objects.

Fig. 7: Bird view of our projected road lane marks for labelling.

Labelling of lane mark segments on road. In self-driving, lane marks are information solely from static background. Fortunately, our collected survey-grade 3D points not only have high density, but also contain lighting intensity, dependent on which we can distinguish the lane mark on the roads. Specifically, we perform similar labelling process as 3D labelling of rigid background by labelling each 3D point to pre-defined lane mark labels listed in Tab. IV.

Nevertheless, different from labelling 3D point clusters where point clouds from buildings and trees are important, for lane marks, we only need to consider points on the road. Therefore, we take out the road point clouds based on normal directions, and perform orthogonal projection of these points from the bird view to a high resolution 2D image, as shown in Fig. 7, over which labellers draw a polygon for each lane mark on the road. In the meantime, our tool brings out the corresponding images, and highlights the regions in 2D for each labelled polygon, where the color and type of the labelled lanemark can be determined.

Labelling of instance segments. Thanks to an active labelling component with detection, it is easy for us to generalize the segmentation label map to produce instance masks given the segmented results from the object detection and segmentation networks. Specifically, we ask the labellers to refine the boundary between different instances when it is necessary, i.e.visually significantly not aligned with true object boundaries.

4 Deep localization and segmentation

Fig. 8: DeLS-3D overview. The black arrows show the testing process, and red arrows indicate the rendering (projection) operation in training and inference. The yellow frustum shows the location of cameras inside the 3D map. The input of our system contains a sequence of images and corresponding GPS/IMU signals. The outputs are the semantically segmented images, each with its refined camera pose.

As discussed in introduction (Sec. 1), ApolloScape contains various ground truth which enables multitask learning. In this paper, we show such a case by creating a deep learning based system for joint localization and semantic segmentation given a semantic 3D map [76], which we call DeLS-3D, as illustrated in Fig. 8. Specifically, at upper part, a pre-built 3D semantic map is available. During testing, an online stream of images and corresponding coarse camera poses from GPS/IMU are fed into the system. Firstly, for each frame, a semantic label map is rendered out given the input coarse camera pose, which is fed into a pose CNN jointly with the respective RGB image. The network calculates the relative rotation and translation, and yields a corrected camera pose. To incorporate the temporal correlations, the corrected poses from pose CNN are fed into a pose RNN to further improves the estimation accuracy in the stream. Last, given the rectified camera pose, a new label map is rendered out, which is fed together with the image to a segment CNN. The rendered label map helps to segment a spatially more accurate and temporally more consistent result for the image stream of video. In this system, since our data contains ground truth for both camera poses and segments, it can be trained with strong supervision at each end of outputs. The code for our system will be released at

. In the following, we will describe the details of our network architectures and the loss functions to train the whole system.

4.1 Camera localization with motion prior

Translation rectification with road prior. One common localization priori for navigation is to use the 2D road map, by constraining the GPS signals inside the road regions. We adopt a similar strategy, since once the GPS signal is out of road regions, the rendered label map will be totally different from the street-view of camera, and no correspondence can be found by the network.

To implement this constraint, firstly we render a 2D road map image with a rasterization grid of from our 3D semantic map by using only road points, i.e.points belong to car-lane, pedestrian-lane and bike-lane etc. Then, at each pixel in the 2D map, an offset value is pre-calculated indicating its 2D offset to the closest pixel belongs to road through the breath-first-search (BFS) algorithm efficiently.

During online testing, given a noisy translation , we can find the closest road points w.r.t. using from our pre-calculated offset function. Then, a label map is rendered based on the rectified camera pose, which is fed to pose CNN.

Fig. 9: The GRU RNN network architecture for modeling a sequence of camera poses.

CNN-GRU pose network architecture. As shown in Fig. 8, our pose networks contain a pose CNN and a pose GRU-RNN. Particularly, the CNN of our pose network takes as inputs an image and the rendered label map from corresponding coarse camera pose

. It outputs a 7 dimension vector

representing the relative pose between the image and rendered label map, and we can get a corrected pose w.r.t. the 3D map by . For the network architecture of pose CNN, we follow the design of DeMoN [54], which has large kernel to obtain bigger context while keeping the amount of parameters and runtime manageable. The convolutional kernel of this network consists a pair of 1D filters in and

-direction, and the encoder gradually reduces the spatial resolution with stride of 2 while increasing the number of channels. We list the details of the network in our implementation details at Sec. 


Additionally, since the input is a stream of images, in order to model the temporal dependency, after the pose CNN, a multi-layer GRU with residual connection 

[77] is appended. More specifically, we adopt a two layer GRU with 32 hidden states as illustrated in Fig. 9. It includes high order interaction beyond nearby frames, which is preferred for improve the pose estimation performance. In traditional navigation applications of estimating 2D poses, Kalman filter [78] is commonly applied by assuming either a constant velocity or acceleration. In our case, because the vehicle velocity is unknown, transition of camera poses is learned from the training sequences, and in our experiments we show that the motion predicted from RNN is better than using a Kalman filter with a constant speed assumption, yielding further improvement over the estimated ones from our pose CNN.

Fig. 10: Architecture of the segment CNN with rendered label map as a segmentation priori. At bottom of each convolutional block, we show the filter size, and at top we mark the downsample rates of each block w.r.t. the input image size. The ’softmax’ text box indicates the places a loss is calculated. Details are in Sec. 4.2.

Pose loss. Following the PoseNet [47], we use the geometric matching loss for training, which avoids the balancing factor between rotation and translation. Formally, given a set of point cloud in 3D , and the loss for each image is written as,


where and are the estimated pose and ground truth pose respectively. is a projective function that maps a 3D point to 2D image coordinates. is the semantic label of and is a weight factor dependent on the semantics. Here, we set stronger weights for point cloud belong to certain classes like traffic light, and find it helps pose CNN to achieve better performance. In [47], only the 3D points visible to the current camera are applied to compute this loss to help the stableness of training. However, the amount of visible 3D points is still too large in practical for us to apply the loss. Thus, we pre-render a depth map for each training image with a resolution of using the ground truth camera pose, and use the back projected 3D points from the depth map for training.

4.2 Video parsing with pose guidance

Having rectified pose at hand, one may direct render the semantic 3D world to the view of a camera, yielding a semantic parsing of the current image. However, the estimated pose is not perfect, fine regions such as light poles can be completely misaligned. Other issues also exist. For instance, many 3D points are missing due to reflection, e.g.regions of glasses, and points can be sparse at long distance. Last, dynamic objects in the input cannot be represented by the projected label map, yielding incorrect labelling at corresponding regions. Thus, we propose an additional segment CNN to tackle these issues, while taking the rendered label map as segmentation guidance.

Segment network architecture. As discussed in Sec. 2, heavily parameterized networks such as ResNet are not efficient enough for our online application. Thus, as illustrated in Fig. 10, our segment CNN is a light-weight network containing an encoder-decoder network and a refinement network, and both have similar architecture with the corresponding ones used in DeMoN [54] including 1D filters and mirror connections. However, since we have a segment guidance from the 3D semantic map, we add a residual stream (top part of Fig. 10), which encourages the network to learn the differences between the rendered label map and the ground truth. In [79], a full resolution stream is used to keep spatial details, while here, we use the rendered label map to keep the semantic spatial layout.

Another notable difference for encoder-decoder network from DeMoN is that for network inputs, shown in Fig. 10, rather than directly concatenate the label map with input image, we transform the label map to a score map through one-hot operation, and embed the score of each pixel to a 32 dimensional feature vector. Then, we concatenate this feature vector with the first layer output from image, where the input channel imbalance between image and label map is alleviated, which is shown to be useful by previous works [80]. For refinement network shown in Fig. 10, we use the same strategy to handle the two inputs. Finally, the segment network produces a score map, yielding the semantic parsing of the given image.

We train the segment network first with only RGB images, then fine-tune the network by adding the input of rendered label maps. This is because our network is trained from scratch, therefore it needs a large amount of data to learn effective features from images. However, the rendered label map from the estimated pose has on average 70 pixel accuracy, leaving only 30 of pixels having effective gradients. This could easily drive the network to over fit to the rendered label map, while slowing down the process towards learning features from images. Finally, for segmentation loss, we use the standard softmax loss, and add intermediate supervision right after the outputs from both the encoder and the decoder as indicated in Fig. 10.

5 Experiments

In this section, we first evaluate our online deep localization and segmentation algorithms (DeLS-3D) on two of our released roads, which is a subset of our full data. We compare it against other SOTA deep learning based visual localization, i.e.PoseNet [9], and segmentation algorithms i.e.ResNet38 [73], which shows the benefits of multitask unification.

Then, we elaborate the benchmarks setup online with ApolloScape and the current leading results, which follows many standard settings such as the ones from KITTI [2] and Cityscapes [3]. These tasks include semantic segmentation, semantic instance segmentation, self-localization, lanemark segmentation. Due to the “DeLS” algorithm proposed in this work does not follow those standard experimental settings, we could not provide its results for the benchmarks. Nevertheless, for each benchmark, we either ran a baseline result with SOTA methods or launched a challenge for other researchers, providing a reasonable estimation of the task difficulties.

5.1 Evaluate DeLS-3D

In this section, we evaluate various settings for pose estimation and segmentation to validate each component in the DeLS-3D system. For GPS and IMU signal, despite we have multiple scans for the same road segments, it is still very limited for training. Thus, follow [52], we simulate noisy GPS and IMU by adding random perturbation

w.r.t. the ground truth pose following uniform distributions. Specifically, translation and rotation noise are set as

and respectively. We refer to realistic data [81] for setting the noisy range of simulation.

Datasets. Two roads early collected at Beijing in China are used in our evaluation. The first one is inside a technology park, named zhongguancun park (Zpark), and we scanned 6 rounds during different daytimes. The 3D map generated has a road length around 3, and the distance between consecutive frames is around 5 to 10. We use 4 rounds of the video camera images for training and 2 for testing, yielding 2242 training images and 756 testing images. The second one we scanned 10 rounds and 4km near a lake, named daoxianghu lake (Dlake), and the distance between consecutive frames is around 1 to 3. We use 8 rounds of the video camera images for training and 2 for testing, yielding 17062 training images and 1973 testing images. The existing semantic classes in the two datasets are shown in Tab. VI, which are subsets from our full semantic classes.

Implementation details. To quickly render from the 3D map, we adopt OpenGL to efficiently render a label map with the z-buffer handling. A 512 608 image can be generated in 70ms with a single Titan Z GPU, which is also the input size for both pose CNN and segment CNN. For pose CNN, the filter sizes of all layers are , and the forward speed for each frame is 9ms. For pose RNN, we sample sequences with length of 100 from our data for training, and the speed for each frame is 0.9ms on average. For segment CNN, we keep the size the same as input, and the forward time is 90ms. Both of the network is learned with ’Nadam’ optimizer [82] with a learning rate of

. We sequentially train these three models due to GPU memory limitation. Specifically, for pose CNN and segment CNN, we stops at 150 epochs when there is no performance gain, and for pose RNN, we stops at 200 epochs. For data augmentation, we use the imgaug

222 library to add lighting, blurring and flipping variations. We keep a subset from training images for validating the trained model from each epoch, and choose the model performing best for evaluation.

For testing, since input GPS/IMU varies every time, i.e. , we need to have a confidence range of prediction for both camera pose and image segment, in order to verify the improvement of each component we have is significant. Specifically, we report the standard variation of the results from a 10 time simulation to obtain the confidence range. Finally, we implement all the networks by adopting the MXNet [83] platform.

Evaluation metrics. We use the median translation offset and median relative angle [9]. For evaluating segment, we adopt the commonly used pixel accuracy (Pix. Acc.), mean class accuracy (mAcc.) and mean intersect-over-union (mIOU) as that from [73].

Data Method Trans (m) Rot () Pix. Acc()


Noisy pose 3.45 0.176 7.87 1.10 54.01 1.5
Pose CNN w/o semantic 1.355 0.052 0.982 0.023 70.99 0.18
Pose CNN w semantic 1.331 0.057 0.727 0.018 71.73 0.18
Pose RNN w/o CNN 1.282 0.061 1.731 0.06 68.10 0.32
Pose CNN w KF 1.281 0.06 0.833 0.03 72.00 0.17
Pose CNN-RNN 1.005 0.044 0.719 0.035 73.01 0.16


Pose CNN w semantic 1.667 0.05 0.702 0.015 87.83 0.017
Pose RNN w/o CNN 1.385 0.057 1.222 0.054 85.10 0.03
Pose CNN-RNN 0.890 0.037 0.557 0.021 88.55 0.13
TABLE V: Compare the accuracy of different settings for pose estimation from the two datasets. Noisy pose indicates the noisy input signal from GPS, IMU, and ’KF’ means kalman filter. The number after

indicates the standard deviation (S.D.) from 10 simulations.

means lower the better and higher the better respectively. We can see the improvement is statistically significant.
Data Method


Pix. Acc




















ResNet38 [73] 64.66 95.87 93.6 98.5 82.9 87.2 61.8 46.1 41.7 82.0 37.5 26.7 45.9 49.5 60.0 85.1 67.3 38.0 89.2 66.3
Render PoseRNN 32.61 73.1 - 91.7 50.4 62.1 16.9 6.6 5.8 30.5 8.9 6.7 10.1 16.3 22.2 70.6 29.4 20.2 73.5 -
SegCNN w/o Pose 68.35 95.61 94.2 98.6 83.8 89.5 69.3 47.5 52.9 83.9 52.2 43.5 46.3 52.9 66.9 87.0 69.2 40.0 88.6 63.8
SegCNN w pose GT 79.37 97.1 96.1 99.4 92.5 93.9 81.4 68.8 71.4 90.8 71.7 64.2 69.1 72.2 83.7 91.3 76.2 58.9 91.6 56.7
SegCNN w Pose CNN 68.6 95.67 94.5 98.7 84.3 89.3 69.0 46.8 52.9 84.9 53.7 39.5 48.8 50.4 67.9 87.5 69.9 42.8 88.5 60.9
SegCNN w Pose RNN 69.93 95.98 94.9 98.8 85.3 90.2 71.9 45.7 57.0 85.9 58.5 41.8 51.0 52.2 69.4 88.5 70.9 48.0 89.3 59.5
Data Method


Pix. Acc




















SegCNN w/o Pose 62.36 96.7 95.3 96.8 12.8 21.5 81.9 53.0 44.7 65.8 52.1 87.2 55.5 66.8 94.5 84.9 20.3 28.9 78.4 82.1
SegCNN w pose GT 73.10 97.7 96.8 97.5 41.3 54.6 87.5 70.5 63.4 77.6 70.5 92.1 69.2 77.4 96.1 87.4 24.5 43.8 80.0 85.7
SegCNN w pose RNN 67.00 97.1 95.8 97.2 30.0 37.4 84.2 62.6 47.4 65.5 62.9 89.6 59.0 70.3 95.2 86.8 23.9 34.4 76.8 86.6
TABLE VI: Compare the accuracy of different segment networks setting over Zpark (top) and Dlake (bottom) dataset. is short for ’traffic’ in the table. Here we drop the 10 times S.D. to save space because it is relatively small. Our results are especially good at parsing of detailed structures and scene layouts, which is visualized in Fig. 11.
Fig. 11: Results from each intermediate stage out of the system over Zpark dataset. Label map is overlaid with the image. Improved regions are boxed and zoomed out (best in color). More results are shown in the online videos for Zpark and Dlake.

Pose Evaluation. In Tab. V, we show the performance of estimated translation and rotation from different model variations. We first directly follow the work of PoseNet [9, 47], and use their published code and geometric loss (Eq. (3)) to train a model on Zpark dataset. Due to scene appearance similarity of the street-view, we did not obtain a reasonable model, i.e. results better than the noisy GPS/IMU signal. At the 1st row, we show the median error of GPS and IMU from our simulation. At the 2nd row, by using our pose CNN, the model can learn good relative pose between camera and GPS/IMU, which significantly reduces the error (60 for , 85 for . By adding semantic cues, i.e.road priori and semantic weights in Eq. (3), the pose errors are further reduced, especially for rotation (from to at the 3rd row). In fact, we found the most improvement is from semantic weighting, while the road priori helps marginally. In our future work, we would like to experiment larger noise and more data variations, which will better validate different cues.

For evaluating an video input, we setup a baseline of performing RNN directly on the GPS/IMU signal, and as shown at ’Pose RNN w/o CNN’, the estimated is even better than pose CNN, while is comparably much worse. This meets our expectation since the speed of camera is easier to capture temporally than rotation. Another baseline we adopt is performing Kalman filter [78] to the output from Pose CNN by assuming a constant speed which we set as the averaged speed from training sequences. As shown at ’Pose CNN w KF’, it does improve slightly for translation, but harms rotation, which means the filter over smoothed the sequence. Finally when combining pose CNN and RNN, it achieves the best pose estimation both for and . We visualize some results at Fig. 11(a-c). Finally at bottom of Tab. V, we list corresponding results on Dlake dataset, which draws similar conclusion with that from Zpark dataset.

Segment Evaluation. At top part of Tab. VI, we show the scene parsing results of Zpark dataset. Firstly, we adopt one of the SOTA parsing network on the CityScapes, i.e. ResNet38 [73], and train it with Zpark dataset. It utilizes pre-trained parameters from the CityScapes [3] dataset, and run with a 1.03 per-frame with our resolution. As shown at the 1st row, it achieve reasonable accuracy compare to our segment CNN (2nd row) when there is no pose priori. However, our network is 10x faster. At 3rd row, we show the results of rendered label map with the estimated pose from pose RNN. Clearly, the results are much worse due to missing pixels and object misalignment. At 4th row, we use the rendered label map with ground truth pose as segment CNN guidance to obtain an upper-bound for our segmentation performance. In this case, the rendered label map aligns perfectly with the image, thus significantly improves the results by correct labelling most of the static background. At 5th and 6th row, we show the results trained with rendered label map with pose after pose CNN and pose RNN respectively. We can see using pose CNN, the results just improve slightly compare to the segment CNN. From our observation, this is because the offset is still significant for some detailed structures, e.g.light-pole.

However, when using the pose after RNN, better alignment is achieved, and the segment accuracy is improved significantly especially for thin structured regions like pole, as visualized in Fig. 11, which demonstrates the effectiveness of our strategy. We list the results over Dlake dataset with more object labelling at bottom part of Tab. VI, and here the rendered label provides a background context for object segmentation, which also improve the object parsing performance.

In Fig. 11, we visualize several examples from our results at the view of camera. In the figure, we can see the noisy pose (a), is progressively rectified by pose CNN (b) and pose RNN (c) from view of camera. Additionally, at (d) and (e), we compare the segment results without and with camera pose respectively. As can be seen at the boxed regions, the segment results with rendered label maps provide better accuracy in terms of capturing region details at the boundary, discovering rare classes and keeping correct scene layout. All of above could be important for applications, e.g. figuring out the traffic signs and tele-poles that are visually hard to detect.

5.2 Benchmarks and baselines

With various tasks and large amount of labelled data we have proposed, it would be non-practical for us to extensively explore algorithms over all of them. Therefore, we release the data to research community, and set up standard evaluation benchmarks. Currently, four challenges have been set up online for evaluation by withholding part of our labelled results as test set, which include semantic segmentation [19], instance segmentation [20], self-localization [22], lanemark segmentation [21].

For evaluation, in the tasks of semantic segmentation, lanemark segmentation, we adopt mean IoU, and in the task of self-localization, we adopt median translation and rotation offset, which are described in evaluation of DeLS-3D (Sec. 5.1

). For the task of instance segmentation, we use interpolated average precision (AP) 

[84] under various IoU thresholds which is used for the COCO challenge [35].Later, we will describe the split of each dataset, the leading method on each benchmark currently.

Semantic segmentation.

For video semantic segmentation, until now, we haven’t receive valid results from the challenge. This probably is due to the extremely large amount of training videos in ApolloScape, making training a model with SOTA deep learning models such as ResNet 

[58] not-practical. Thus, we select a subset from the whole data for comparison of one model performance between ApolloScape and Cityscapes. Specifically, 5,378 training images and 671 testing images are carefully selected from our 140K labelled semantic video frames for setting up the benchmark, which maintains the diversity and objects appeared of the collected scenes. The selected images will be released at our website [19].

We conducted our experiments using ResNet-38 network [73] that trades depth for width comparing with the original ResNet structure [58]. We fine-tune their released model using our training with initial learning rate 0.0001, standard SGD with momentum 0.9 and weight decay 0.0005, random crop with size , 10 times data augmentation that includes scaling and left-right flipping, and we train the network for 100 epochs. The predictions are computed with the original image resolution without any post-processing steps such as multi-scale ensemble etc.. Tab. VII shows the parsing results of classes in common for these two datasets. Notice that using exactly same training procedure, the test IoU with our dataset are much lower than that from the Cityscapes mostly due to the challenges we have mentioned at Sec. 3.2, especially for movable objects, where mIoU is 34.6% lower than the corresponding one for the Cityscapes.

Here, we leave the training a model with the our full dataset to the research community and our future work.

Group Class Cityscapes ApolloScape
movable car 94.67 87.12
object motorcycle 70.51 27.99
bicycle 78.55 48.65
person 83.17 57.12
rider 65.45 6.58
truck 62.43 25.28
bus 88.61 48.73
mIoU 77.63 43.07
surface road 97.94 92.03
sidewalk 84.08 46.42
infrastructure fence 61.49 42.08
traffic light 70.98 67.49
pole 62.11 46.02
traffic sign 78.93 79.60
wall 58.81 8.41
building 92.66 65.71
nature vegetation 92.41 90.53
TABLE VII: Results of image parsing based on ResNet-38 network.

Instance segmentation. This task is an extension of semantic object parsing by jointly considering detection and segmentation. Specifically, we select 39212 training images and 1907 testing images, and set up a challenge benchmark online[20] evaluating 7 objects in our dataset (Upper part of Tab. VII) to collect potential issues within autonomous driving scenario. During the past few month, there are over 140 teams attended our challenge, which reveals our community is much more interested in object level understanding rather than scene segmentation.

The leading results from our participants are shown in Tab. IX, where we can see in general the reported mAP of winning teams are lower than those reported in Cityscapes benchmarks 333, by using similar strategies [85] modified from MaskRCNN [13]. Based on the challenge reports from the winning team [86], comparing to Cityscapes, ApolloScape contains more tiny and occluded objects (60 object has scale less than pixels), which leads to significant drop of performance when transfer models trained on other datasets.

Method mIOU











ResNet38 [73] 40.0 48.6 53.1 57.8 52.1 22.7 36.4 18.7 59.1 40.4 27.1








49.1 57.4 20.9 0.01 0.9 36.1 40.5
TABLE VIII: The IoU over different classes using one SOTA semantic segmentation architecture, i.e.ResNet38 [73]. Here,“s_w_d” is short for solid, white and dividing in Tab. IV by combining the first letter of type, color and usage respectively, and other classes are named accordingly. Here the amount of class is less than that in Tab. IV due to zero accuracy over some predefined labels.

Lanemark segmentation. Lanemark segmentation task follows the same metric as semantic segmentation, which contains 132189 training images and 33790 testing images. Our in-house challenge benchmark [21] chooses to evaluate 35 most common lane mark types on the road as listed in Tab. IV.

Until the submission of this paper, we only have one work based on ResNet-38 network [73] evaluated, probably due to the large amount of data (160K+ images). We show the corresponding detailed results in Tab. VIII, where we can see the mIoU of each class are still very limited () comparing to the accuracy of leading semantic segmentation algorithms on general classes. We think this is mostly because the high contrast, dimmed and broken lane marks on the road such as the cases shown in Fig. 3. We wish in the near future, more research could be evolved in this task to improve the visual perception.

metric  [86]  [87]  [88]
mAP 33.97 30.22 25.02
TABLE IX: Results of image parsing based on ResNet-38 network.
Road ID Trans (m) Rot ()
Road11 0.0476 0.0452
Road12 0.1115 0.0528
Road14 0.0785 0.0825
Road15 0.0711 0.1240
Road16 0.1229 0.2063
Road17 0.4934 0.3135
mean 0.1542 0.1374
TABLE X: Detailed localization accuracy of the leading results on our benchmark from [89].

Self-localization. We use the same metrics for evaluating camera pose, i.e.median offset of translation and rotation, as described in Sec. 5.1. This task contains driving videos in 6 sites from Beijing, Guangzhou, Chengdu and Shanghai in China, under multiple driving scenarios and day times. In total, we provide 153 training videos and 71 testing videos including over 300k image frames, and build an in-house challenge benchmark website [22] most recently.

Currently, we also have few submissions, while the leading one published is from one of the SOTA method for large-scale image based localization [89]

. The method is based on image retrieval with learned deep features via various triplet losses. We show their reported number in Tab. 

X, where the localization errors are surprisingly small, i.e. translation is around 15cm and rotation error is around 0.14 degree. Originally, we believe image appearance similarity on the street or highway can fail deep network models. However, from the participant results, especially designed features distinguish minor appearance changes and provide high accurate localization results. Another possibility is that our acquisition vehicle always drives in a roughly constant speed, reducing the issues from speed changing in real applications. In the near future, hopefully, we can add more challenging scenarios with more variations in driving speed and weathers.

In summary, from the dataset benchmarks we set up and evaluated algorithms, we found for low-level localization, the results are impressively good, while for high level semantic understanding, Apolloscape provides additional challenges and new issues, yielding limited accuracy for SOTA algorithms, i.e. best mAP is around for instance segmentation, and best mIoU is around for lane segmentation. Comparing to human perception, visual based algorithms for autonomous driving definitely need further research to handle extremely difficult cases.

6 Conclusion and Future Work

In this paper, we present ApolloScape, a large, diverse, and multi-task dataset for autonomous driving, which includes high density 3D point cloud map, per-pixel, per-frame semantic image label, lane mark label, semantic instance segmentation for various videos. Every frame of our videos is geo-tagged with high accurate GPS/IMU device. ApolloScape is significantly larger than existing autonomous driving datasets, e.g.  KITTI [2] and Cityscapes [3], yielding more challenges for computer vision research field. In order to label such a large dataset, we developed an active 2D/3D joint annotation pipeline, which effectively accelerate the labelling process. Back on ApolloScape, we developed a joint localization and segmentation algorithm with a 3D semantic map, which fuses multi-sensors, is simple and runs efficiently, yielding strong results in both tasks. We hope it may motivate researcher to develop algorithms handling multiple tasks simultaneously by considering their inner geometrical relationships. Finally, for each individual task, we set up an online evaluation benchmark where different algorithms can compete with a fair platform.

Last but not the least, ApolloScape is an evolving dataset, not only in terms of data scale, but also in terms of various driving conditions, tasks and acquisition devices. For example, firstly, we plan to enlarge our dataset to contain more diversified driving environments including snow, rain, and foggy. Secondly, we are in the progress of labelling 3D cars, 3D humans and tracking of each object in 3D. Thirdly, we plan to mount a panoramic camera system, and Velodyne [4] in near future to generate depth maps for objects and panoramic images.


This work is supported by Baidu Inc.. We also thank the work of Xibin Song, Binbin Cao, Jin Fang, He Jiang, Yu Zhang, Xiang Gu, and Xiaofei Liu for their laborious efforts in organizing data, helping writing label tools, checking labelled results and manage the content of benchmark websites.


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