Structure Guided Proposal Completion for 3D Object Detection
3D object detection from point clouds is one of the key components in autonomous driving. Current two-stage detectors generate a small number of proposals, and then refine them in the second RCNN procedure. However, due to the inherent sparsity of point clouds, the first stage may predict some low quality proposals with incomplete structure and inaccurate localization. These low quality proposals fail to obtain adequate and precise proposal features which are essential for the following refinement, inevitably degrading the overall detection performance. To alleviate this problem, we propose Structure guided Proposal Completion (SPC) for 3D object detection from point clouds. Specifically, two completion strategies are developed to obtain high quality proposals: one is Structure Completion, in which a group of structural proposals are obtained by traversing most structures, and thus at least one proposal with ground truth similar structure can be guaranteed. The other is RoI Feature Completion, which is used to fill the empty area of proposals with virtual points under structure-aware manner. With the proposed SPC, high quality proposals with clearer structure and more precise localization can be obtained, and further promote the RCNN to perceive adequate proposal features. Extensive experiments on KITTI benchmark demonstrate the effectiveness of our proposed method, especially for hard setting objects with fewer LiDAR points.