Analysis of Point Cloud Domain Gap Effects for 3D Object Detection Evaluation

Engineering notes

Key topics: autonomous driving system, autonomous driving, 3d object detection, object detection, lidar, point cloud, carla. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

: The development of autonomous driving systems heavily relies on high-quality LiDAR data, which is essential for robust object detection and scene understanding. Nevertheless, obtaining a substantial amount of such data for effective training and evaluation of autonomous driving algorithms is a major challenge. To overcome this limitation, recent studies are taking advantage of advancements in realistic simulation engines, such as CARLA, which have provided a breakthrough in generating synthetic LiDAR data that closely resembles real-world scenarios. However, these data are far from being identical to real data. In this study, we address the domain gap between real LiDAR data and synthetic data. We train deep-learning models for object detection using real data. Then, those models are rigorously evaluated using synthetic data generated in CARLA. By quantifying the discrepancies between the model’s performance on real and synthetic data, the present study shows that there is indeed a domain gap between the two types of data and does not affect equal to different model architectures. Finally, we propose a method for synthetic data processing to reduce this domain gap. This research contributes to enhancing the use of synthetic data for autonomous driving systems.

Links and sources

• Official / arXiv page
• PDF from original source

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