Pattern-Aware Data Augmentation for LiDAR 3D Object Detection

11/30/2021
by   Jordan S. K. Hu, et al.
0

Autonomous driving datasets are often skewed and in particular, lack training data for objects at farther distances from the ego vehicle. The imbalance of data causes a performance degradation as the distance of the detected objects increases. In this paper, we propose pattern-aware ground truth sampling, a data augmentation technique that downsamples an object's point cloud based on the LiDAR's characteristics. Specifically, we mimic the natural diverging point pattern variation that occurs for objects at depth to simulate samples at farther distances. Thus, the network has more diverse training examples and can generalize to detecting farther objects more effectively. We evaluate against existing data augmentation techniques that use point removal or perturbation methods and find that our method outperforms all of them. Additionally, we propose using equal element AP bins to evaluate the performance of 3D object detectors across distance. We improve the performance of PV-RCNN on the car class by more than 0.7 percent on the KITTI validation split at distances greater than 25 m.

READ FULL TEXT
research
11/20/2022

Context-Aware Data Augmentation for LIDAR 3D Object Detection

For 3D object detection, labeling lidar point cloud is difficult, so dat...
research
10/07/2022

Resolving Class Imbalance for LiDAR-based Object Detector by Dynamic Weight Average and Contextual Ground Truth Sampling

An autonomous driving system requires a 3D object detector, which must p...
research
03/18/2023

3D Data Augmentation for Driving Scenes on Camera

Driving scenes are extremely diverse and complicated that it is impossib...
research
07/19/2022

Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness

Accurate 3D object detection with LiDAR is critical for autonomous drivi...
research
03/20/2023

DR.CPO: Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion

In autonomous driving, data augmentation is commonly used for improving ...
research
07/27/2020

Part-Aware Data Augmentation for 3D Object Detection in Point Cloud

Data augmentation has greatly contributed to improving the performance i...
research
02/06/2022

LiDAR dataset distillation within bayesian active learning framework: Understanding the effect of data augmentation

Autonomous driving (AD) datasets have progressively grown in size in the...

Please sign up or login with your details

Forgot password? Click here to reset