  The performance of point cloud 3D object detection hinges on effectively
representing raw points, grid-based voxels or pillars. Recent two-stage 3D
detectors typically take the point-voxel-based R-CNN paradigm, i.e., the first
stage resorts to the 3D voxel-based backbone for 3D proposal generation on
bird-eye-view (BEV) representation and the second stage refines them via the
intermediate point representation. Their primary mechanisms involve the
utilization of intermediary keypoints to restore the substantial 3D structure
context from the converted BEV representation. The skilled point-voxel feature
interaction, however, makes the entire detection pipeline more complex and
compute-intensive. In this paper, we take a different viewpoint -- the
pillar-based BEV representation owns sufficient capacity to preserve the 3D
structure. In light of the latest advances in BEV-based perception, we devise a
conceptually simple yet effective two-stage 3D detection architecture, named
Pillar R-CNN. On top of densified BEV feature maps, Pillar R-CNN can easily
introduce the feature pyramid architecture to generate 3D proposals at various
scales and take the simple 2D R-CNN style detect head for box refinement. Our
Pillar R-CNN performs favorably against state-of-the-art 3D detectors on the
large-scale Waymo Open Dataset but at a small extra cost. It should be
highlighted that further exploration into BEV perception for applications
involving autonomous driving is now possible thanks to the effective and
elegant Pillar R-CNN architecture.
