PVFE: Point-Voxel Feature Encoders for 3D Object Detection

3D object detection is a challenging problem in 3D computer vision. In this paper, a Point-Voxel Feature Encoder (PVFE) is proposed to obtain the spatial context information between points and voxels for 3D object detection in point clouds. Firstly, the point cloud is converted into 3D voxel grids to avoid loss of spatial structure information in projecting. Then, voxel-wise features are learned through PVFE, which is consisting of stacked fully connected layer. Last, voxel-wise features are processed by the Sparse Convolutional Layers and as the input of RPN which performs category prediction and bounding box regression. Experiments of car, bicyclist and pedestrian detection have been conducted respectively on the KITTI benchmark. Experimental results have clearly shown the superiority of the proposed network.