SampleDet3D: Sample Enhanced 3D Object Detection

Center-based 3D object detection has underperformed recently compared to advanced techniques. We experimentally find that the root lies in two weaknesses of the basic sample mechanism: (1) Unreasonable assignment that close-range and high-frequency objects dominate the network optimization since samples are equally assigned to each object. (2) Ambiguous encoding that samples exhibit suboptimal object discrimination ability, as the encoding process is restricted to a limited receptive field. To realize a reasonable assignment, Dynamic Multi-Quality Assignment (DMQA) is proposed, which dynamically assigns and supervises samples through fine-grained control. Concretely, initial samples are defined based on prior attributes (category and distance) per object, and dynamically adjusted upon the learning effect (classification and localization confidence). Besides, multi-scale auxiliary losses are introduced, ensuring precise sample learning. As for ambiguous encoding, Interactive Enhancement (IE) is introduced to improve sample representation through cross-task and cross-sample interaction. Cross-task interaction first aggregates neighborhood context from another task map. Parallel attention further performs cross-sample interaction on both local and global levels. Based on DMQA and IE, we propose a novel 3D detector named Sample Enhanced 3D Object Detection (SampleDet3D). Comprehensive experiments demonstrate that SampleDet3D effectively enhances center-based detection and achieves state-of-the-art performance on both Waymo and ONCE datasets.