论文标题
征服：3D对象检测的查询对比度素 - det
ConQueR: Query Contrast Voxel-DETR for 3D Object Detection
论文作者
论文摘要
尽管基于DITR的3D检测器可以简化检测管道并实现直接的稀疏预测，但它们的性能仍然落在密集检测器后面，并通过从点云进行后处理进行后处理。 Detr通常比GTS（例如300个查询v.s. 40个对象）在一个场景中采用更多的查询，这不可避免地会在推理过程中引起许多误报。在本文中，我们提出了一个简单而有效的稀疏3D检测器，称为查询对比度体素 - det（征服），以消除具有挑战性的误报，并实现更准确和更稀疏的预测。我们观察到，由于缺乏明确的监督来区分本地相似的查询，大多数假阳性在本地地区高度重叠。因此，我们提出了一种查询对比机制，以明确提高其对所有无与伦比的查询预测的最佳匹配GT的查询。这是通过为每个GT构建正和负GT Query Pairs的构建，以及基于特征相似性增强正面的GT Query Pairs与负面的阳性损失。征服缩小了稀疏和密集的3D探测器的间隙，并减少了约60％的假阳性。我们的单帧征服在具有挑战性的Waymo Open数据集验证集上实现了新的最先进（SOTA）71.6 MAPH/L2，以超过2.0 MAPH/L2的优于以前的SOTA方法（例如PV-RCNN ++）。
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.
