Non-maximum suppression (NMS) is an essential post-processing module used in
many 3D object detection frameworks to remove overlapping candidate bounding
boxes. However, an overreliance on classification scores and difficulties in
determining appropriate thresholds can affect the resulting accuracy directly.
To address these issues, we introduce fuzzy learning into NMS and propose a
novel generalized Fuzzy-NMS module to achieve finer candidate bounding box
filtering. The proposed Fuzzy-NMS module combines the volume and clustering
density of candidate bounding boxes, refining them with a fuzzy classification
method and optimizing the appropriate suppression thresholds to reduce
uncertainty in the NMS process. Adequate validation experiments are conducted
using the mainstream KITTI and large-scale Waymo 3D object detection
benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS
module can improve the accuracy of numerous recently NMS-based detectors
significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is
particularly evident for small objects such as pedestrians and bicycles. As a
plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no
obvious increases in inference time.

通过引入模糊学习到非极大值抑制（NMS）中，我们提出了一种新的广义 Fuzzy-NMS 模块，以实现对候选边界框的更细致过滤，通过模糊分类方法将体积和聚类密度相结合，优化适当的抑制阈值并减少 NMS 过程中的不确定性，通过对 KITTI 和 Waymo 等基准测试进行充分验证实验，结果表明所提出的 Fuzzy-NMS 模块能够显著提高许多基于 NMS 的检测器的准确性，尤其对于行人和自行车等小目标。作为即插即用模块，Fuzzy-NMS 无需重新训练并且不会显著增加推论时间。