S-VoteNet: Deep Hough Voting with Spherical Proposal for 3D Object Detection

Current 3D object detection methods adopt an analogous box prediction structure with the 2D methods, which predict center and size of the object simultaneously in a box regression procedure, leading to the poor performance of 3D detector to a great extent. In this work, we propose S-VoteNet, which converts the prediction of 3D bounding box into two parts: center prediction and size prediction. By introducing a novel spherical proposal, S-VoteNet uses vote groups to predict the center and radius of object rather than all parameters of 3D bounding box. The prediction of radius is used to constrain the object size, and the radius-based spherical center loss is applied to measure the geometric distance between the proposal and ground-truth. To make better use of the geometric information provided by point cloud, S-VoteNet aggregates seeds by the votes indices to generate seed groups. The seed groups are then used for box size regression and orientation estimation. By decoupling the localization and size estimation, our method effectively reduces the regression burden of the 3D detector. Experimental results on SUN RGB-D 3D detection benchmark demonstrate that our S-VoteNet achieves state-of-the-art performance by using only point cloud as input.