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R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes

by   Stefano Gasperini, et al.

While self-supervised monocular depth estimation in driving scenarios has achieved comparable performance to supervised approaches, violations of the static world assumption can still lead to erroneous depth predictions of traffic participants, posing a potential safety issue. In this paper, we present R4Dyn, a novel set of techniques to use cost-efficient radar data on top of a self-supervised depth estimation framework. In particular, we show how radar can be used during training as weak supervision signal, as well as an extra input to enhance the estimation robustness at inference time. Since automotive radars are readily available, this allows to collect training data from a variety of existing vehicles. Moreover, by filtering and expanding the signal to make it compatible with learning-based approaches, we address radar inherent issues, such as noise and sparsity. With R4Dyn we are able to overcome a major limitation of self-supervised depth estimation, i.e. the prediction of traffic participants. We substantially improve the estimation on dynamic objects, such as cars by 37 demonstrating that radar is a valuable additional sensor for monocular depth estimation in autonomous vehicles. Additionally, we plan on making the code publicly available.


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1 Introduction

Depth estimation is a fundamental task for scene understanding in autonomous driving and robotics navigation. While learning-based supervised approaches for monocular depth have achieved strong performance in outdoor scenarios 

[7, 2], the expensive LiDAR sensors required for supervision are not readily available. Additionally, collecting such ground truth data is challenging, and requires further processing, as the raw LiDAR signal may not be sufficient.

Alternative methods exploit geometrical constraints on stereo pairs or monocular videos to learn depth in a self-supervised fashion. Image sequences offer the most inexpensive source of supervision for this task. However, these approaches require to estimate the camera pose between frames at training time [11], and suffer from inherent issues, such as scale ambiguity and the tendency to incorrectly estimate the depth of dynamic objects.

Figure 1: Example of depth prediction of our R4Dyn compared to that of Monodepth2 [11], from the validation set of nuScenes [3]. This dynamic scene violates the static world assumption, posing a challenge for self-supervised approaches. The depth of the safety critical oncoming traffic is severely underestimated by Monodepth2, but correctly predicted by our R4Dyn.
Figure 2: Overview of R4Dyn. The proposed approach incorporates radar as a weak supervision signal, and optionally as additional input. R4Dyn extends a self-supervised framework by incorporating radar to improve on the depth estimation of dynamic objects.

Furthermore, self-supervised methods rely on the assumption of a moving camera in a static world [30]. As real-world street scenes are typically dynamic, this assumption is often violated, leading to significantly wrong predictions. This raises a variety of issues, such as the infinite depth problem, where leading vehicles driving at the same speed as the camera are predicted to be infinitely far away (such as the horizon), due to their lack of relative motion across frames. Several works addressed this problem either by excluding from the loss computation those regions where no pixel variation is detected [11], or discarding from the training set those frames with leading vehicles driving at a similar speed [13]. Others increase the complexity by estimating the individual object’s motion [4] or the scene flow [20]. However, existing methods that preserve the model complexity [11, 13] fail to cope with oncoming traffic, as can be seen in Figure 1. In this case, instead of infinitely far, the estimated depth tends to be greatly underestimated, since from the camera perspective its relative motion across frames is larger than that of static objects.

Compared to LiDARs, radars are relatively inexpensive range sensors, already integrated in a large number of mass production vehicles [21, 23], to aid features such as adaptive cruise control. Nevertheless, radar popularity in the learning-based autonomous driving domain is yet limited, and has been explored mainly in the context of object detection [26, 17]. To this date and to the best of our knowledge, only two works [21, 23] investigated the use of radar to improve depth estimation, both proposing a multi-network pipeline to incorporate radar data at inference time, while using LiDAR during training.

In this paper, we aim to bridge this gap and integrate readily available radar sensors to improve self-supervised monocular depth estimation. As radars are already very common, our proposal allows to collect training data from a wide range of existing vehicles, instead of a few prototypes. Furthermore, despite their inherent noise and sparsity, radars could provide enough information to mitigate the limitations of self-supervised approaches, overcoming the need for LiDARs. Towards this end, we propose a novel loss formulation to complement self-supervised approaches, showing the benefits of radar as additional weak supervision signal to improve the depth prediction of dynamic objects. Moreover, we optionally integrate radar data at inference time, transforming the task into very sparse depth completion. We name our method R4Dyn, and the contributions of this paper can be summarized as follows:

  • We use radar to aid the prediction of dynamic objects in self-supervised monocular depth estimation.

  • To the best of our knowledge, this is the first monocular depth estimation work that exploits radar as supervision signal.

  • We propose a technique to filter and expand radar detections, and make radar compatible with learning-based solutions.

  • We provide extensive evaluations, including errors on safety critical dynamic objects, on the challenging nuScenes dataset [3], training various prior methods under equivalent settings, hence creating a new benchmark, and easing the comparisons for future works.

2 Related Work

2.1 Supervised Monocular Depth Estimation

Estimating depth from a single color image is an ill-posed problem, as there is an infinite number of 3D scenes that can yield the same 2D projection. Nonetheless, tremendous advances have been achieved since Eigen et al. [6] pioneered using CNN-based architectures and Laina et al. [19]

leveraged fully-convolutional networks with residual connections 

[14] to predict dense depth maps from monocular images. While most supervised works regressed directly to the depth measurements of LiDAR sensors (as in KITTI [9]) or RGB-D cameras (as in NYU-Depth v2 [28]), Fu et al. [7] formulated the task in an ordinal fashion.

2.1.1 Depth Completion with Radar

Despite a recently increasing interest in radar for object detection [26, 17], to this date, only two works [21, 23] used it for depth estimation. Both achieved substantial improvements by using it in a LiDAR-supervised setting with a multi-stage architecture, with the first stage filtering the radar signal and improving its quality. In particular, by incorporating the radar as additional input, they transformed the depth estimation task into highly sparse depth completion. Lin et al. [21] were the first to use radar in this supervised context, where they followed a late-fusion approach to account for the heterogeneity of input modalities. Long et al. [23] proposed a sophisticated learning-based association between the projected radar points and the RGB image.

Similarly to these pioneering works [21, 23]

, our method also incorporates radar for depth estimation, but we follow a novel idea: we focus on using radar to improve the estimation of dynamic objects in a self-supervised setting, for which we propose a specific loss function.

2.2 Self-Supervised Monocular Depth Estimation

Self-supervised methods overcome the need for expensive LiDAR data by leveraging view reconstruction constraints, either via stereo pairs [8, 10] or monocular videos [34, 11, 12]. The latter build on the motion parallax induced by a moving camera in a static world [30]. Furthermore, these methods require to predict simultaneously the depth and the camera pose transformation. Since the pioneering work on video-based training by Zhou et al. [34], vast improvements have been achieved thanks to novel loss terms [11], detail-preserving network architectures [12] and the exploitation of cross-task dependencies [16, 13].

2.2.1 Solutions to Self-supervised Inherent Issues

Scale ambiguity Since infinitely many 3D objects correspond to the same 2D projection, video-based methods can only predict depth up to an unknown scale factor. Therefore, a plethora of works [34, 11, 4] rely on ground truth (i.e. LiDAR) median-scaling at test time. Guizilini et al. [12] targeted this issue by imposing a weak velocity supervision on the estimated pose transformation, exploiting the available odometry information, achieving scale-awareness.

Dynamic scenes Another major limitation of video-based approaches is due to the inherent static world assumption. This is perpetually violated in driving scenarios, leading to critically incorrect depth predictions of dynamic objects (e.g. traffic participants). A typical failure case is caused by leading vehicles driving at a similar speed as the ego vehicle, thereby lacking relative motion across frames and resulting in a significantly overestimated depth. Godard et al. [11] addressed this problem with an auto-masking loss to ignore pixels without relative motion. In contrast, Guizilini et al. [13] proposed a workaround to detect and discard training samples where this ”infinite depth problem” occurs, thereby keeping only uncomplicated frames, albeit reducing the training data.

However, neither of the two [11, 13] accounted for oncoming traffic, which leads to a significantly underestimated depth, since its motion across frames is larger than that of the static elements. This might be linked to the popular KITTI depth benchmark [9] mostly lacking such kind of safety critical dynamic scenes, widely available on nuScenes [3]. Alternative solutions target dynamic scenes, by increasing the model complexity and simultaneously predicting depth, ego-pose, plus 3D motion of dynamic objects [4] or scene flow [33, 24, 15, 20].

Although methods learning scene flow in a self-supervised fashion do not require expensive additional labels, e.g. instance segmentation [4], they might suffer from the same ambiguities of self-supervised depth estimation, and require stereo vision [33, 24, 15].

Our proposed approach is significantly different from previous works targeting dynamic scenes. We address this problem by incorporating radar data, which has not been explored before in this context.

3 Method

Figure 3: Radar preparation for weak supervision, detail of the top right portion of Figure 2. The signal is accumulated, then filtered via 2D boxes, duplicated and expanded.

In this paper we incorporate radar data to enhance the self-supervised depth prediction of dynamic scenes. An overview of our method can be seen in Figure 2. We build on top of a self-supervised framework which learns from monocular videos, and uses the vehicle odometry (Section 3.1), as in [12], for scale-awareness. Although radar signals are highly sparse and noisy, they could provide enough information to improve self-supervised methods. Along this line, we integrate radar data both during training to improve the prediction of dynamic objects (Section 3.2.3), and during inference to increase the overall robustness (Section 3.3). However, it is first necessary to make radar data compatible with learning-based approaches, by tackling noise and sparsity. As we use the signal as weak supervision during training, it is crucial to eliminate noise (Section 3.2.2), and also expand its influence across a large area (Section 3.2.1).

3.1 Self-Supervised Framework

The proposed method is built on top of a video-based monocular depth approach, described in this Section. We aim to simultaneously predict the depth of a target frame and the pose transformations between target and source frames . Depth and pose estimates serve to warp the source frames into a reconstructed target view, from which an appearance-based error is computed from the view reconstruction [34] and a Structural Similarity (SSIM) [32] term, as in [11, 12].

Following [11], only the minimum reprojection error is considered, accounting for partial occlusions. Moreover, pixels without relative motion across frames are masked out [11]. An additional loss encourages smoothness whilst preserving edges [10]. and are computed at each scale of the depth decoder, after upsampling to full resolution [11].

As the radar provides absolute depth values, to use its signal as weak supervision (Section 3.2.3), it is crucial to estimate depth at the right scale. However, as the learning objectives above only allow to predict depth up to an unknown scale factor, we follow [12] to achieve scale-awareness with a weak velocity supervision on the pose transformation.

Figure 4: Depth gradient preserving mechanism. We compute between prediction and radar detection, then adjust each pixel in the prediction by , by enforcing a pseudo-ground truth during training, thereby preserving the predicted gradient.

3.2 Weak Radar Supervision

In this work, we focus on dynamic objects, which embody safety critical failure cases of self-supervised monocular depth estimation. We aim to mitigate this issue, which is visible in Figure 1, via a weak radar-based supervision.

3.2.1 Addressing Radar Sparsity

Radar detections projected to the image plane occupy a single pixel, leading to a low density of , compared to of a 32-beam LiDAR [3]. To address this high sparsity and make radar suitable as supervision signal, it is fundamental to expand its influence over a larger portion of the image. In particular, as we focus on dynamic objects, we aim to expand the radar signal across all object pixels within their boundaries. At training time, we overcome sparsity by incorporating the RGB image context information, similarly to [29, 1], as well as 2D bounding boxes.

Method Sup. Input AbsRel SqRel RMSE AbsRel AbsRel AbsRel AbsRel
Lin et al. [21] GT ImR 0.1086 1.080 5.394 88.21 0.1907 0.2082 0.2088 0.2930
Struct2Depth [4] Mi Im 0.2195 3.799 8.441 73.23 0.3323 0.3516 0.3739 0.2993
PackNet-SfM [12] Mv Im 0.1567 2.440 7.230 82.64 0.1814 0.2382 0.2508 0.2473
Monodepth2 [11] M Im 0.1398 1.911 6.825 84.82 0.1983 0.2110 0.2300 0.2572
baseline [ours] Mv Im 0.1315 1.705 6.520 85.71 0.1862 0.2091 0.2254 0.2351
R4Dyn-L [ours] Mvr Im 0.1296 1.658 6.536 85.76 0.1343 0.1618 0.1686 0.2231
R4Dyn-LI [ours] Mvr ImR 0.1259 1.661 6.434 86.97 0.1250 0.1504 0.1589 0.2146

Table 1: Evaluation on the nuScenes [3] validation day-clear set.

were pretrained on ImageNet 

[5], also on KITTI [9]. Supervisions (Sup.): GT: via LiDAR data, M: via monocular sequences, : test-time median-scaling via LiDAR, i: instance masks, v: weak velocity, r: weak radar. Inputs (In.): Im: RGB, ImR: RGB and radar. C, V, N and P for Cars, Vehicles, Non-parked vehicles and Pedestrians, respectively. R4Dyn-L and R4Dyn-LI: proposed method with radar as L: weak supervision, I: input. Notation reused in other Tables.

Context-aware radar expansion We expand each radar point to cover a larger area of the corresponding object. Expanding it as much as possible, but constraining it within the boundaries, and accounting for 3D shape variations require a precise mapping between the projected radar points and the object pixels. Towards this end, during training, we generate an association map around each radar point. We exploit the idea of bilateral filtering [29], a common edge-preserving image smoothing technique. In particular, we link an image pixel to a radar point if and are spatially close in the image space, within the same bounding box, and have similar pixel intensities (i.e. ). This bilateral association map can be formalized as follows:


where and are the pixel distances, the difference of intensities of a radar point pixel and a neighboring pixel , while and denote domain and range smoothing parameters, respectively. Hence, the bilateral association

represents the probability of a pixel

to be linked to a radar point .

Figure 5: The automask [11] and our radar supervision mask. Red pixels contribute to the loss . The automask correctly masks out the leading car (top), but not the oncoming one (bottom). Our radar supervision acts (green) successfully on both cars.

Pixel-radar association map Although this bilateral confidence map preserves edges by design, it may still leak small, nonzero values beyond object boundaries, leading to undesirably smooth depth predictions. We avoid this by clipping the heatmaps in proximity of the box edges. Moreover, could serve as per-pixel weight for the radar supervision. However, this would put more emphasis on the radar pixel and result in uneven object depth maps. We address this by transforming the confidence values in a binary map. Considering the set of reliable detections (Section 3.2.2) and a threshold , we compute:


such that all relevant portions receive the same amount of supervision.

Duplication To account for the missing elevation information of many radar sensors (e.g. in nuScenes [3]), we copy each projected radar point and its corresponding depth along the vertical axis to the lower third and middle of its bounding box. We leave out the upper half of the box to consider depth variations within the boxes (e.g. windshield of the car in the lower part of Figure 5).

Measurement Accumulation To address the high sparsity of radar signals, we also accumulate multiple measurements. Towards this end, we leverage radar doppler information to compensate for ego- and target-motion across samples, as proposed by [17]. Since the doppler velocity is radial, it provides only a rough estimate of the true velocity of the target, but it is a reasonable approximation considering the frame rate. Through duplication and accumulation, we obtain a dense set of radar points .

3.2.2 Addressing Radar Noise

Radar suffers from several sources of noise (clutter), complicating its usage in learning-based approaches. The major origins of clutter are multi-path [23] and ”see-through” effects, due to the different viewpoints [27] and the physical sensor properties.

Clutter removal We want to extract from reliable measurements for supervision. We do so by leveraging 2D bounding boxes during training to filter out noisy radar detections. Within each object in the image space, radar detections closer to the sensor are likely to be reliable, whereas points at higher distances often result from noise. Hence, to obtain , we find the minimum depth per bounding box and only keep detections within having . The tolerance allows to keep multiple points in , and accounts for depth variations along 3D objects.

Object-focused filtering Radars without elevation information (e.g. nuScenes [4]) provide only detections parallel to the ground plane, thus, in the image, further points appear higher than closer ones, and within the same objects are more likely to be noisy. Analogously, detections around the box edges could be unreliable due to wide boxes or ”see-through” effects. For these reasons, we discard points in the upper 50% and outer 20% of bounding boxes, as well as overlapping areas. Furthermore, it is more intricate to assess radar detections reliability outside object boxes, hence we discard all background radar points to avoid erroneous supervision.

3.2.3 Training Objective for Dynamic Objects

Addressed noise and sparsity, the radar signal is suitable to weakly supervise the depth prediction of dynamic objects. We want to use it to adjust erroneous depth estimations via a loss function. We define this weak radar supervision as:


where denotes the Hadamard product. Eq. 3 aims at fixing the predicted depth towards the expanded target radar measurements , for each of the image pixels where the binary association map (Eq. 2) is positive.

Depth gradient preservation The expanded measurements in Eq. 3 act as ground truth depth. We introduce as the area affected by the expanded radar point . If was constant within each , then would shift the whole area to the same depth . This would not take into account depth variations within objects, such as the door panels or the windshield of the lower car in Figure 5 being further than its bumper. Moreover, although the depth of dynamic objects is often under- or over-estimated, depth variations within objects are typically well predicted (e.g. in Figure 1). For these reasons, we adapt the pseudo-ground truth to preserve depth variations. As shown in Figure 4, we do so by computing as the difference between the prediction and the measurement , only at each radar pixel . We generate the pseudo ground truth by shifting the prediction by the same :


where is computed at the radar pixel such that .

The final objective function can thus be formulated as:


with , and being balancing coefficients.

ID Weak radar sup. AbsRel AbsRel
L1 baseline 0.1315 0.1862
L2 L1 + w/ raw pts 0.1766 0.4841
L3 L2 + filter pts w/ GT box 0.1765 0.4842
L4 L3 + bilateral expansion 0.1306 0.1551
L5 L4 + binary mapping 0.1297 0.1356
L6 L5 + depth gradient pres. 0.1296 0.1343
L7 L6 – GT + predicted box 0.1289 0.1343
Table 2: Weak radar supervision ablation, evaluated on the nuScenes [3] day-clear validation set. The baseline L1 is defined in Section 3.1. L3-L6 and L7 make use of 2D boxes from ground truth annotations or an off-the-shelf detector [31], respectively. L6 is equivalent to R4Dyn-L.

3.3 Sparse Depth Completion with Radar as Input

As radar sensors are readily available in a wide range of mass-production vehicles [21, 23], a depth estimator could exploit them at inference time, transforming the estimation task in a very sparse depth completion problem. To do so, we first need to mitigate radar inherent sparsity and noise issues. We follow a similar approach as for using the signal as weak supervision (Section 3.2), although we do not make use of 2D bounding boxes at inference time, since extracting them for the input would increase the runtime.

For sparsity, we apply the same measurement accumulation technique described in Section 5 to obtain a denser point cloud, aggregating the current and past radar frames. For the noise, we adopt the same min-pooling approach as in Section 3.2.2, but instead of exploiting 2D boxes, we slide a fixed-size window across all projected radar points.

To account for the fact that radar and camera are heterogeneous sensors, we apply separate encodings and merge the features in a late fusion fashion, as in [21].

4 Experiments and Results

4.1 Experimental Setup

Dataset We conduct all experiments on the challenging nuScenes dataset [3]. We selected it as it is the only available large-scale public dataset with the recording vehicle fitted with a camera and an automotive radar. NuScenes contains around 15h of driving data collected in Boston and Singapore, including diverse traffic scenarios (e.g. both left and right hand drive), with a plethora of dynamic scenes (unlike KITTI [9]), making it difficult for self-supervised depth estimation. As we are interested in sensor setups readily available in production cars, we consider only data from camera and radar mounted at the front of the vehicle. For training, we use only scenes with good visibility (i.e. day-clear in the dataset). This includes 15129 samples (i.e. synchronized frames) for the official training set and 6019 for the official validation set (of which 4449 are day-clear).

ID Input config. AbsRel AbsRel
I1 RGB only 0.1315 0.1862
I2 I1 + 1 radar input 0.1357 0.1926
I3 I2 + 0.1298 0.1319
I4 I3 + radar accum. 0.1301 0.1279
I5 I4 + doppler accum. 0.1273 0.1264
I6 I5 + filtered 0.1259 0.1250
Table 3: Pre-processing ablation for radar as input, evaluated on the day-clear nuScenes [3] validation set. I1 is defined in Section 3.1. I6 is equivalent to R4Dyn-LI, our full approach.

Evaluation metrics We evaluated our models on the standard depth estimation metrics and errors. As ground truth we used single raw LiDAR scans up to a maximum depth of . In particular, as this work focuses on dynamic objects, we are interested in the performance improvements on such objects. Towards this end, we also evaluated according to the semantic class: we exploited LiDAR semantic segmentation annotations from [3] to distinguish between classes, and computed the errors on the depth predictions at the corresponding LiDAR points. The evaluated classes comprise Cars, Vehicles (e.g. Cars, Trucks, Buses, Motorcycles, Bicycles), Non-parked Vehicles and Pedestrians, thereby encompassing all traffic participants.

Network architecture The presented techniques for processing and incorporating radar data for depth are not tailored to a specific backbone architecture. We used the small and effective ResNet-18-based [14] pose and depth network from [11]. The pose network comprised 13.0M parameters, while the depth network 14.8M. With the radar as input, an additional ResNet-18 encoder branch was used, increasing the depth parameters by 1.1M.

Implementation details Each training sample consisted of a 576x320 resolution image triplet (), 4 radar measurements at for the input, and 7 radar sweeps for the supervision at training time at . At inference time we used a single image at , and 4 radar sweeps at . Using the Adam optimizer [18] with ,

, we trained with a batch size of 16 for a total of 40 epochs with initial learning rate

, which was halved every 10 epochs. If enabled, we introduced the radar supervision after 30 epochs, when the correct depth scale had already been learned, and set the learning rate to , halved after 8 epochs. The loss balancing weights were set to , , . During training, we applied random horizontal flipping to the input data as well color jittering with brightness, contrast, saturation and hue

to the input images. The window for filtering radar at inference time was 320 pixels tall and 8 wide, with stride 3, while the tolerance


. Further details can be found in the supplementary material. In the following, we refer to different configurations of our method, namely R4Dyn-L, R4Dyn-I, and R4Dyn-LI, with -L and -I denoting our weak radar supervision and radar as input, respectively. We trained all models using PyTorch on a single NVIDIA Tesla V100 32GB GPU. Inference of our full method (i.e. R4Dyn-LI) takes 27ms on an NVIDIA GTX 1080 8GB GPU.

Radar % AbsRel AbsRel AbsRel AbsRel
0 % 0.1315 0.1862 0.2091 0.2351
25 % 0.1309 0.1358 0.1672 0.2254
50 % 0.1261 0.1309 0.1553 0.2167
100 % 0.1259 0.1250 0.1504 0.2146
Table 4: Evaluation on the nuScenes [3] day-clear validation set. Different amounts of radar points are shown, both for input and weak supervision. 0% is the baseline, while 100% is R4Dyn-LI. C, V and P stand for Cars, Vehicles and Pedestrians respectively.

Prior works and baseline For a fair comparison, we retrained all methods on the same dataset split, using the same image resolution, the official implementations and parameters, until convergence. For Struct2Depth [4]

we started from model weights pretrained on ImageNet 

[5] and then KITTI [9] by the authors, which improved its convergence over ImageNet-pretraining only. All methods except for PackNet-SfM [12] used a ResNet-18 [14] backbone pretrained on ImageNet [5]. Our baseline is the self-supervised image-only method described in Section 3.1, which includes the weak velocity supervision.

Figure 6: Qualitative results from related works on the nuScenes [3] validation set. Next to each method name we indicate its supervision.

4.2 Quantitative Results

Comparison with related methods Table 1 shows the comparison between our R4Dyn and related approaches. We report alternative solutions for dynamic objects, such as Struct2Depth [4] and Monodepth2 [11], as well as another method using radar for depth, i.e. the LiDAR-supervised work by Lin et al. [21]. Our R4Dyn-L and R4Dyn-LI outperformed the strong baseline across the board, by a significant margin. Remarkably, the error on Cars dropped by a substantial 33% with R4Dyn-LI, showing the benefit of radar for estimating the depth of dynamic objects. Our approach significantly improved over Monodepth2 [11], on which our baseline builds upon. As can also be seen in Figure 5, the automask from Monodepth2 [11] was not able to cope with oncoming traffic, occurring frequently in the dataset. This led to a large error on Cars and other traffic participants. Our R4Dyn-L and R4Dyn-LI reduced it by 32% and 37% respectively. Struct2Depth [4], despite being additionally pretrained on KITTI [9] and its individual motion predictions, produced the worst estimations, with large inconsistencies (AbsRel std. 0.1511). Our R4Dyn-LI outputs were far more consistent (std. 0.079). We attribute this difference to the superiority of radar over instance masks as weak supervision, as well as to Struct2Depth [4] not fully solving the ”infinite depth problem” (e.g. in Figure 6). The sophisticated PackNet architecture [12] was not able to deliver satisfactory results, which could be due to the larger model size (129M instead of 15M for ResNet-18), and the relatively small dataset. This motivated using ResNet [14] as backbone. Furthermore, the LiDAR-supervised work by Lin et al. [21], with radar as input, performed better than ours overall, albeit worse on safety critical traffic participants, such as by 53% on Cars. This could be due to the sparsity of the LiDAR from which it learned. Overall, Table 1 demonstrates the benefit of radar for monocular depth estimation, as it can substantially improve the predictions of safety critical dynamic objects, both as weak supervision with R4Dyn-L and as input with R4Dyn-LI.

Method all clear rain night
Lin et al. [21] 0.126 0.109 0.145 0.248
Struct2Depth [4] 0.238 0.220 0.271 0.336
PackNet-SfM [12] 0.168 0.157 0.177 0.262
Monodepth2 [11] 0.161 0.140 0.193 0.287
baseline [ours] 0.146 0.132 0.166 0.242
R4Dyn-L [ours] 0.147 0.130 0.164 0.273
R4Dyn-LI [ours] 0.137 0.126 0.146 0.219
Table 5: Evaluation of AbsRel to generalize on adverse and unseen conditions of the validation set of nuScenes [3]. All methods were trained on day-clear (clear).

Weak Radar supervision ablation study Table 2 shows the impact of the various components of our weak radar supervision. Throughout the table, errors do not decrease with the introduction of each and every feature, thereby confirming that the radar signal necessitates to be filtered (row L3) and expanded (L4) before it can positively contribute over the baseline (L1), while simply integrating the raw points (L2) increased the errors. With L7, we show that 2D bounding boxes can be extracted via an off-the-shelf detector, such as Scaled-YOLOv4 [31]

trained on MS COCO 

[22], removing the need for ground truth annotations. L7 improved over L6 (which used ground truth boxes), presumably due to the nuScenes [3] boxes being oversized (used in L3 to L6). Overall, Table 2 confirms the importance of our modifications to use radar as weak supervision.

Radar as input ablation study Table 3 reports various configurations for using radar as input. Again motivating the expansion (I4 and I5) and filtering (I6) of the radar signal. In fact, simply adding a single radar sweep in input (I2) did not outperform the baseline (I1).

Variable amount of radar signal In Table 4 we show how the output quality changed by excluding a variable amount of radar detections from the dataset. Already 25% of the radar detections brought a large improvement over the baseline (0%), overall as well as on safety critical traffic participants. Once again, this shows the benefit of radar for depth estimation, despite its inherent noise and sparsity. In particular, adding more detections (filtered and expanded), systematically reduced all the errors. Moreover, considering the rapid progressing of sensor technology [25], higher resolution (e.g. 200%, 400%) and less noisy automotive radar sensors might be available in the future, which would further increase the gap to the RGB-only baseline.

Generalization to unseen adverse conditions Table 5 reports AbsRel of our R4Dyn and related methods on unseen illumination and weather conditions. This shows the ability of each to generalize to different data distributions. Our R4Dyn-LI outperformed all self- and weakly-supervised approaches in all conditions. Compared to the LiDAR-supervised work by Lin et al. [21], R4Dyn-LI performs similarly in rain and significantly better in night scenes, reiterating the effectiveness and robustness of our proposed techniques.

4.3 Qualitative Results

Qualitative results in Figure 6 confirm the findings of our experiments, showing the superiority of our R4Dyn in estimating the depth of traffic participants. In particular, Struct2Depth [4] had frequent issues with leading vehicles (first 3 scenes), and added a halo effect to the oncoming car in the fourth scene. Monodepth2 [11] was able to correctly estimate leading vehicles, thanks to its automask, but not oncoming traffic (as seen in Figure 1 and inspected in Figure 5), which resulted in severe underestimations (first, second and fourth scene). The LiDAR-supervised work by Lin et al. [21] correctly estimated the overall depth, but missed most details, delivering blurred outputs. Instead, our R4Dyn could accurately estimate all challenging dynamic scenes, preserving sharp details.

5 Conclusion

In this paper we proposed R4Dyn, a set of techniques to integrate radar into a self-supervised monocular depth estimation framework. Extensive experiments showed the benefit of using radar both during training as weak supervision, and at inference time as added input. Our method substantially improved on the prediction of safety critical traffic participants over all related works. Therefore, R4Dyn constitutes a valuable step towards robust depth estimation. Additionally, the inexpensive and readily available setup required, allows to collect training data from a variety of existing vehicles, removing the need for expensive LiDAR data.

Appendix A Supplementary Material

In this Section we include further details and additional class-wise results computed from the same models reported in the manuscript.

a.1 Self-supervised Framework

In the following, we further specify the loss functions described in Section 3.1. The photometric loss is a combination of -loss and SSIM [32], as in [10]:


where balances between the and the SSIM term. Additionally, as in [11], we only consider the minimum reprojection error to account for partial occlusions:


Furthermore, we follow [11] by automatically masking out pixels which do not change appearance in between frames:


Hence, the photometric loss is only considered in regions where . Moreover, to encourage local smoothness while preserving edges we use a specific term from [10]:


where denotes the element-wise absolute value, and are gradients in x- and y-direction, and the mean-normalized inverse of the depth prediction.

As described in Section 3.1, we follow [12] with a weak velocity supervision for scale-awareness:


where and are the predicted and ground truth pose translations respectively, easily obtainable from readily available velocity information (e.g. via odometry).

Figure 7: The histogram shows a class-wise evaluation of our R4Dyn and Monodepth2 [11] on the nuScenes day-clear validation set [3]. Other includes all remaining classes, that are not traffic participants, and NP indicates Non-Parked Cars or Vehicles. Lower absolute relative (AbsRel) error is better.
Method all clear rain night
Lin et al. [21] 0.217 0.191 0.225 0.433
Struct2Depth [4] 0.346 0.332 0.320 0.535
PackNet-SfM [12] 0.219 0.181 0.270 0.449
Monodepth2 [11] 0.232 0.199 0.261 0.486
baseline [ours] 0.212 0.186 0.240 0.391
R4Dyn-L [ours] 0.164 0.134 0.219 0.311
R4Dyn-LI [ours] 0.140 0.125 0.171 0.204

Lin et al. [21] 0.229 0.208 0.233 0.428
Struct2Depth [4] 0.361 0.352 0.335 0.535
PackNet-SfM [12] 0.259 0.238 0.274 0.440
Monodepth2 [11] 0.240 0.211 0.270 0.483
baseline [ours] 0.231 0.209 0.259 0.384
R4Dyn-L [ours] 0.182 0.162 0.220 0.308
R4Dyn-LI [ours] 0.159 0.150 0.176 0.200

Lin et al. [21] 0.292 0.293 0.254 0.401
Struct2Depth [4] 0.298 0.299 0.276 0.367
PackNet-SfM [12] 0.252 0.247 0.284 0.355
Monodepth2 [11] 0.258 0.257 0.253 0.317
baseline [ours] 0.238 0.235 0.244 0.348
R4Dyn-L [ours] 0.229 0.223 0.255 0.409
R4Dyn-LI [ours] 0.218 0.215 0.234 0.332
Table 6: Evaluation of AbsRel on Cars, Vehicles and Pedestrians under adverse conditions of the validation set of nuScenes [3]. All methods were trained only on scenes with day-clear (clear) conditions. This Table complements Table 5, therefore shows the ability of each method to generalize to unseen settings.
Cl. Method Sup. Input AbsRel SqRel RMSE RMSE


Lin et al. [21] GT ImR 0.1907 2.399 6.922 0.2460 75.71 91.02 96.06
Struct2Depth [4] M Im 0.3323 7.436 9.353 0.3307 57.38 80.54 89.98
PackNet-SfM [12] Mv Im 0.1814 1.936 6.313 0.2341 72.88 91.02 96.46
Monodepth2 [11] M Im 0.1983 2.100 6.635 0.2509 68.23 88.76 94.81
baseline [ours] Mv Im 0.1862 2.115 6.735 0.2495 70.41 87.96 94.41
R4Dyn-L [ours] Mvr Im 0.1343 1.481 5.713 0.1913 81.45 94.02 97.32
R4Dyn-LI [ours] Mvr ImR 0.1250 1.371 5.395 0.1813 84.14 94.38 97.43


Lin et al. [21] GT ImR 0.1898 2.485 6.793 0.2418 77.15 90.69 95.65
Struct2Depth [4] M Im 0.3703 9.063 10.05 0.3472 54.42 78.15 88.35
PackNet-SfM [12] Mv Im 0.1902 2.050 6.397 0.2394 70.56 90.35 96.20
Monodepth2 [11] M Im 0.2130 2.368 6.905 0.2624 65.05 87.03 93.97
baseline [ours] Mv Im 0.1862 2.115 6.735 0.2495 70.41 87.96 94.41
R4Dyn-L [ours] Mvr Im 0.1356 1.518 5.651 0.1894 80.92 93.56 97.16
R4Dyn-LI [ours] Mvr ImR 0.1274 1.399 5.302 0.1793 83.80 94.01 97.43


Lin et al. [21] GT ImR 0.2330 3.336 8.456 0.2728 65.46 85.48 93.44
Struct2Depth [4] M Im 0.3962 7.487 12.11 0.4057 41.22 69.19 85.83
PackNet-SfM [12] Mv Im 0.3434 7.328 11.60 0.3662 52.96 77.66 88.94
Monodepth2 [11] M Im 0.2442 4.518 9.781 0.3007 63.13 83.44 91.84
baseline [ours] Mv Im 0.2547 4.614 9.586 0.3080 62.85 82.28 91.73
R4Dyn-L [ours] Mvr Im 0.2187 3.950 8.719 0.2779 68.08 85.45 93.14
R4Dyn-LI [ours] Mvr ImR 0.2055 3.706 8.316 0.2600 70.35 86.28 93.46


Lin et al. [21] GT ImR 0.2356 3.248 8.410 0.2829 63.70 83.46 92.67
Struct2Depth [4] M Im 0.3711 7.153 12.26 0.4027 45.32 71.41 84.74
PackNet-SfM [12] Mv Im 0.3472 6.561 11.48 0.3758 49.82 75.84 87.82
Monodepth2 [11] M Im 0.2659 5.330 10.42 0.3221 60.76 81.03 91.48
baseline [ours] Mv Im 0.2751 4.782 10.25 0.3340 55.26 80.55 90.05
R4Dyn-L [ours] Mvr Im 0.2457 4.184 9.739 0.3077 58.71 83.26 92.09
R4Dyn-LI [ours] Mvr ImR 0.2369 4.219 9.493 0.2997 63.17 82.85 92.64


Lin et al. [21] GT ImR 0.2529 3.473 8.292 0.2757 62.84 84.27 93.50
Struct2Depth [4] M Im 0.2328 3.110 8.026 0.2854 58.39 82.58 90.00
PackNet-SfM [12] Mv Im 0.2007 2.575 7.062 0.2529 67.38 88.33 93.28
Monodepth2 [11] M Im 0.1900 2.457 6.693 0.2409 72.86 89.33 93.23
baseline [ours] Mv Im 0.1849 2.481 6.856 0.2486 72.11 89.26 92.93
R4Dyn-L [ours] Mvr Im 0.1833 2.534 6.885 0.2496 69.71 89.43 93.05
R4Dyn-LI [ours] Mvr ImR 0.1730 2.401 6.519 0.2389 75.26 89.56 92.64
Table 7: Class(Cl.)-wise evaluation on the main individual Vehicle classes on the nuScenes [3] day-clear validation set. NP stands for Non-Parked. Table to be considered in conjunction with Table 8.
Cl. Method Sup. Input AbsRel SqRel RMSE RMSE


Lin et al. [21] GT ImR 0.2082 2.637 7.400 0.2668 73.08 88.99 95.08
Struct2Depth [4] M Im 0.3516 7.179 10.13 0.3612 54.53 79.25 89.63
PackNet-SfM [12] Mv Im 0.2382 3.393 7.927 0.2885 67.48 87.71 94.14
Monodepth2 [11] M Im 0.2110 2.809 7.617 0.2726 68.89 87.85 94.50
baseline [ours] Mv Im 0.2091 2.680 7.597 0.2775 67.91 87.33 93.91
R4Dyn-L [ours] Mvr Im 0.1618 2.047 6.681 0.2273 77.16 92.72 96.76
R4Dyn-LI [ours] Mvr ImR 0.1504 1.922 6.371 0.2188 80.51 92.77 96.75


Lin et al. [21] GT ImR 0.2088 2.892 7.396 0.2597 74.98 89.46 94.96
Struct2Depth [4] M Im 0.3739 8.440 10.58 0.3643 53.17 77.87 88.58
PackNet-SfM [12] Mv Im 0.2508 3.699 8.044 0.2923 66.24 86.47 93.39
Monodepth2 [11] M Im 0.2300 3.372 7.917 0.2815 66.25 86.16 93.29
baseline [ours] Mv Im 0.2254 3.219 7.885 0.2880 66.67 84.78 92.27
R4Dyn-L [ours] Mvr Im 0.1686 2.461 6.728 0.2268 77.86 91.87 95.97
R4Dyn-LI [ours] Mvr ImR 0.1589 2.311 6.375 0.2162 80.86 92.61 96.16


Lin et al. [21] GT ImR 0.2930 4.496 9.507 0.2966 59.51 84.11 93.09
Struct2Depth [4] M Im 0.2993 5.489 11.48 0.3714 49.37 74.98 87.35
PackNet-SfM [12] Mv Im 0.2473 4.171 9.301 0.2987 61.93 86.17 92.33
Monodepth2 [11] M Im 0.2572 4.420 9.831 0.3087 61.46 86.04 91.88
baseline [ours] Mv Im 0.2351 4.004 9.117 0.2961 66.20 86.37 92.08
R4Dyn-L [ours] Mvr Im 0.2231 3.670 8.806 0.2853 66.90 87.00 92.42
R4Dyn-LI [ours] Mvr ImR 0.2146 3.613 8.560 0.2763 70.74 86.73 92.41


Lin et al. [21] GT ImR 0.2227 2.767 7.319 0.2735 72.33 88.68 95.10
Struct2Depth [4] M Im 0.3400 6.552 9.726 0.3543 56.16 80.38 90.92
PackNet-SfM [12] Mv Im 0.2383 3.405 7.757 0.2861 69.02 88.50 94.75
Monodepth2 [11] M Im 0.2123 2.832 7.592 0.2700 70.11 89.14 95.37
baseline [ours] Mv Im 0.2032 2.548 7.278 0.2693 70.90 88.97 95.11
R4Dyn-L [ours] Mvr Im 0.1631 1.997 6.522 0.2279 78.42 93.22 97.16
R4Dyn-LI [ours] Mvr ImR 0.1551 2.020 6.367 0.2222 81.20 92.73 97.09


Lin et al. [21] GT ImR 0.0781 0.693 4.268 0.1542 93.07 97.35 98.73
Struct2Depth [4] M Im 0.1873 3.254 6.823 0.2645 78.61 90.80 95.42
PackNet-SfM [12] Mv Im 0.1141 1.723 5.777 0.2051 88.68 95.54 97.70
Monodepth2 [11] M Im 0.1117 1.344 5.354 0.1913 89.50 96.54 98.15
baseline [ours] Mv Im 0.1010 1.207 5.223 0.1916 90.73 96.32 97.97
R4Dyn-L [ours] Mvr Im 0.1036 1.194 5.290 0.1931 90.20 96.21 97.93
R4Dyn-LI [ours] Mvr ImR 0.1003 1.197 5.190 0.1886 91.13 96.41 98.00
Table 8: Class(Cl.)-wise evaluation on Vehicles (includes all individual classes from Table 7, plus Bicylces, Construction Vehicles and Emergency Vehicles), Non-Parked Vehicles, Pedestrians, Objects (all object classes together) and Other (pixels that do not correspond to objects) on the nuScenes [3] day-clear validation set. Table to be considered in conjunction with Table 7.

a.2 Radar Accumulation

The accumulation of multiple radar measurements described in Section 5 is performed by mapping points from time to , as proposed by [17]:


where , and denote the estimated target location at time , the ego pose transformation between measurements, and the measured doppler velocity of the target.

a.3 Additional Implementation Details

As we focus on safety critical dynamic objects (e.g. traffic participants), when filtering the radar signal to use it as weak supervision (Section 3.2), we only consider bounding boxes of the following classes of nuScenes [3]: Vehicles (including all sub-classes) and Pedestrians (including all sub-classes). Furthermore, as we want to expand the radar signal over the objects, the binary association mask from Equation 2 should vary in size depending on the object, e.g. larger for a bus, than for a pedestrian. Additionally, we consider as most reliable detections those projected in the lower center of a bounding box. Towards this end, we set the smoothing parameter according to the bounding box dimensions, hence accounting for the object size: we set and , where and are the box width and height, and is a constant scale factor set to . Moreover, is a scale factor that depends on the position of the considered radar point with respect to the bounding box: , with and being the minimum distance of the radar point from the bounding box side and top edges respectively. The range smoothing parameter is fixed to .

For prior works, as mentioned in Section 4.1, we used the original implementation and parameters, adapted as follows to accomodate the different dataset (i.e. nuScenes [3]). We trained Monodepth2 [11] for 40 epochs, which is the same as our R4Dyn. PackNet-SfM [12] was trained for a total of 200 epochs due its to slow convergence, and Struct2Depth [4] was trained for 75 epochs after the full KITTI [9] training performed by the authors. For fairness when comparing with other approaches, for Struct2Depth [4] we used the motion model (denoted by the authors with an M), but not their online refinement (indicated with an R). To ensure fair comparability with other methods, for the work of Lin et al. [21] we only used radar measurements from the past and present, but not from the future, which were used in the original implementation.

a.4 Additional Results

a.4.1 Class-wise Comparison with Related Works

Additionally to the absolute relative error (AbsRel) on object classes Cars, Vehicles, Non-parked Vehicles and Pedestrians reported in Table 1, we provide extensive class-wise results in Tables 7 and 8. Moreover, we plot a comparison between Monodepth2 [11] and our R4Dyn in Figure 7 across the various classes. From the Figure, it can be seen that our approach outperformed Monodepth2 on every object class by a significant margin, especially for Cars, Vehicles and their Non-Parked (NP) variants. Considering Tables 7 and 8, the LiDAR-supervised work by Lin et al. [21], which uses radar as input, was able to deliver superior estimates on non-object classes, denoted as Other (e.g. Driveable Surface and Vegetation). Nevertheless, our R4Dyn could obtain significantly lower errors and better scores on all object classes, except for Trucks, probably due to trucks being often static in urban areas, such as in nuScenes [3]. The results re-emphasize and confirm the effectiveness and the robustness of our approach across various safety critical dynamic objects, as well as the benefit of radar for depth estimation.

a.4.2 Class-wise Comparison on Adverse Conditions

In Table 6 we provide class-wise results of ours and related methods under adverse weather settings. As for Table 5, which reports general errors in the same weather conditions, all approaches were trained on day-clear scenes (indicated as clear in the Table), therefore the values represent the ability of each method to generalize to rather different inputs. In particular, in Table 6 we provide AbsRel errors on safety critical traffic participants: Cars, Vehicles and Pedestrians. For these classes, our R4Dyn-LI was able to outperform related methods by a significant margin, under most settings. Again, we attribute this to the benefit of radar and the effectiveness of our techniques to incorporate it.


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