  Unlike 2D object detection where all RoI features come from grid pixels, the
RoI feature extraction of 3D point cloud object detection is more diverse. In
this paper, we first compare and analyze the differences in structure and
performance between the two state-of-the-art models PV-RCNN and Voxel-RCNN.
Then, we find that the performance gap between the two models does not come
from point information, but structural information. The voxel features contain
more structural information because they do quantization instead of
downsampling to point cloud so that they can contain basically the complete
information of the whole point cloud. The stronger structural information in
voxel features makes the detector have higher performance in our experiments
even if the voxel features don't have accurate location information. Then, we
propose that structural information is the key to 3D object detection. Based on
the above conclusion, we propose a Self-Attention RoI Feature Extractor (SARFE)
to enhance structural information of the feature extracted from 3D proposals.
SARFE is a plug-and-play module that can be easily used on existing 3D
detectors. Our SARFE is evaluated on both KITTI dataset and Waymo Open dataset.
With the newly introduced SARFE, we improve the performance of the
state-of-the-art 3D detectors by a large margin in cyclist on KITTI dataset
while keeping real-time capability.
