  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.
