  Although DETR-based 3D detectors can 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 up to ~60% false positives. Our
single-frame ConQueR achieves new state-of-the-art (sota) 71.6 mAPH/L2 on the
challenging Waymo Open Dataset validation set, outperforming previous sota
methods (e.g., PV-RCNN++) by over 2.0 mAPH/L2.
