DSP-Net: Dense-to-Sparse Proposal Generation Approach for 3D Object Detection on Point Cloud

Object proposals generated based on sparse points from the raw point cloud have been widely used in 3D object detection. However, following the above scheme, most existing proposal generators have two problems, one is that the features for proposal generation constrain the detection performance by containing insufficient information; the other is that the sparse points obtained from the raw point cloud are misaligned with their corresponding objects in location and feature aspects. In this paper, we propose a dense-to-sparse proposal generation approach for 3D object detection, which can deal with the two problems simultaneously. Our approach utilizes the 3D CNN backbone to output dense features as a supplement to the original sparse point features for proposal generation. Besides, an object-aware feature pooling module is designed to address the misalignment between sparse points and corresponding objects. Experiments on the KITTI dataset show that our method outperforms the existing sparse-style methods and other published state-of-the-art methods.