  As a fundamental problem in computer vision, 3D object detection is
experiencing rapid growth. To extract the point-wise features from the
irregularly and sparsely distributed points, previous methods usually take a
feature grouping module to aggregate the point features to an object candidate.
However, these methods have not yet leveraged the surface geometry of
foreground objects to enhance grouping and 3D box generation. In this paper, we
propose the RBGNet framework, a voting-based 3D detector for accurate 3D object
detection from point clouds. In order to learn better representations of object
shape to enhance cluster features for predicting 3D boxes, we propose a
ray-based feature grouping module, which aggregates the point-wise features on
object surfaces using a group of determined rays uniformly emitted from cluster
centers. Considering the fact that foreground points are more meaningful for
box estimation, we design a novel foreground biased sampling strategy in
downsample process to sample more points on object surfaces and further boost
the detection performance. Our model achieves state-of-the-art 3D detection
performance on ScanNet V2 and SUN RGB-D with remarkable performance gains. Code
will be available at https://github.com/Haiyang-W/RBGNet.
