3D Object Detection Enhancement Based on Multi-frame Point Cloud Registration (MF-PCR)

Engineering notes

Key topics: autonomous driving system, autonomous driving, self-driving vehicle, self-driving, 3d object detection, object detection, lidar, point cloud. 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

3D object detection is a crucial feature of autonomous driving systems for localizing and categorizing objects. This feature enables self-driving vehicles and roadside units to perceive their environment accurately. Therefore, the accuracy of 3D object detection has direct impacts on the performance and safety of autonomous driving systems. Many works on 3D object detection employ supervised deep neural networks requiring annotated datasets for training models. However, annotating object bounding boxes in point clouds is a time-consuming and challenging task as LiDAR is affected by occlusions and provides partial views of the environment. This work proposes a pipeline for aligning sparse vehicle point clouds without requiring any annotated data and generating bounding boxes from aggregated point clouds. The pipeline comprises a bounding box estimator for generating rough bounding boxes, initial alignment based on these rough bounding boxes, and point cloud registration combining point-to-point and plane-to-plane methods. The method improves the quality of bounding boxes, achieving a 10% increase in recall at an IoU threshold of 0.7 and outperforming feature-based registration methods in terms of translation error and rotation error as well.

Links and sources

• Official / arXiv page

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