Learned Non-Maximum Suppression for 3D Object Detection 事件

Learned Non-Maximum Suppression for 3D Object Detection arXiv:2606.03568v1 Announce Type: new Abstract: Post-processing is a critical stage in LiDAR-based 3D object detection, where dense and overlapping proposals must be filtered for compact and reliable perception. This work introduces two learned filtering modules that replace heuristic non-maximum suppression (NMS) by leveraging relations among detections. D2D-Rescore employs transformer-based detection-to-detection (D2D) attention, while G