摘要 : 3D point cloud object detection is of great significance in autonomous driving, robotics, and related fields. However, the current algorithms fail to fully consider that positive sample points of a point cloud object exclusively r... 展开 3D point cloud object detection is of great significance in autonomous driving, robotics, and related fields. However, the current algorithms fail to fully consider that positive sample points of a point cloud object exclusively reside on its surface, resulting in decreased accuracy. High-quality classification points are located centrally, while high-quality regression points are predominantly situated at the boundary. We propose an anchor-free object detection algorithm called Valuable Region and Valuable Point (VRVP), aiming to address the inconsistency between the classification and regression tasks. We achieve high-quality object size regression through a classification-based approach and implicitly correlate the regression and classification branches. Furthermore, a valuable region extraction module is introduced to select valuable points and regions and fill in the missing features of the object's center area. We validate the effectiveness of our algorithm on both the KITTI dataset and Waymo dataset by comparing it with state-of-the-art algorithms. The results demonstrate that the proposed method exhibits strong competitiveness by significantly improving detection accuracy for small objects such as pedestrians and cyclists, while maintaining high accuracy in detecting cars. 收起