ConQueR: Query Contrast Voxel-DETR for 3D Object Detection

Benjin Zhu, Zhe Wang, Shaoshuai Shi, Hang Xu, Lanqing Hong, Hongsheng Li; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 9296-9305


Although DETR-based 3D detectors simplify the detection pipeline and achieve direct sparse predictions, their performance still lags behind dense detectors with post-processing for 3D object detection from point clouds. DETRs usually adopt a larger number of queries than GTs (e.g., 300 queries v.s. 40 objects in Waymo) in a scene, which inevitably incur many false positives during inference. In this paper, we propose a simple yet effective sparse 3D detector, named Query Contrast Voxel-DETR (ConQueR), to eliminate the challenging false positives, and achieve more accurate and sparser predictions. We observe that most false positives are highly overlapping in local regions, caused by the lack of explicit supervision to discriminate locally similar queries. We thus propose a Query Contrast mechanism to explicitly enhance queries towards their best-matched GTs over all unmatched query predictions. This is achieved by the construction of positive and negative GT-query pairs for each GT, and a contrastive loss to enhance positive GT-query pairs against negative ones based on feature similarities. ConQueR closes the gap of sparse and dense 3D detectors, and reduces 60% false positives. Our single-frame ConQueR achieves 71.6 mAPH/L2 on the challenging Waymo Open Dataset validation set, outperforming previous sota methods by over 2.0 mAPH/L2. Code:

Related Material

[pdf] [supp] [arXiv]
@InProceedings{Zhu_2023_CVPR, author = {Zhu, Benjin and Wang, Zhe and Shi, Shaoshuai and Xu, Hang and Hong, Lanqing and Li, Hongsheng}, title = {ConQueR: Query Contrast Voxel-DETR for 3D Object Detection}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {9296-9305} }