Pillar in Pillar: Multi-Scale and Dynamic Feature Extraction for 3D Object Detection in Point Clouds

Sparsity and varied density are two of the main obstacles for 3D detection networks with point clouds. In this paper, we present a multi-scale voxelization method and a decomposable dynamic convolution to solve them. We consider the misalignment problem between voxel representation with different scales and present a center-aligned voxelization strategy. Instead of separating points into individual groups, we use an overlapped partition mechanism to avoid the perception deficiency of edge points in each voxel. Based on this multi-scale voxelization, we are able to build an effective fusion network by one-iteration top-down forward. To handle the variation of density in point cloud data, we propose a decomposable dynamic convolutional layer that considers the shared and dynamic components when applying convolutional filters at different positions of feature maps. By modeling bases in the kernel space, the number of parameters for generating dynamic filters is greatly reduced. With a self-learning network, we can apply dynamic convolutions to input features and deal with the variation in the feature space. We conduct experiments with our PiPNet on KITTI dataset and achieve better results than other voxelization-based methods on 3D detection task.