3D物体検出のための学習型非最大値抑制
Learned Non-Maximum Suppression for 3D Object Detection
LiDARベースの3D物体検出において、ヒューリスティックなNMSを学習ベースのフィルタリングモジュールに置き換え、検出精度を向上させる手法を提案した論文。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Timo Osterburg, Stefan Schütte, Torsten Bertram
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
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 GossipNet3D adapts the 2D GossipNet concept to 3D through localized message passing in bird's-eye view. A metric-aware matching strategy aligned with the nuScenes evaluation protocol ensures consistent training and validation behavior, improving overall detection performance. Both approaches improve mean average precision (mAP), nuScenes detection score (NDS), and true positive quality compared to CircleNMS, particularly for small and infrequent classes, while adding minimal computational overhead. These results demonstrate that learned, detection-level filtering can enhance 3D detector reliability without modifying the base network, offering a principled alternative to heuristic suppression. Code is available at https://github.com/rst-tu-dortmund/learned-3d-nms .
