Interpolation-Aware Padding for 3D Sparse Convolutional Neural Networks

Sparse voxel-based 3D convolutional neural networks (CNNs) are widely used for various 3D vision tasks. Sparse voxel-based 3D CNNs create sparse non-empty voxels from input point clouds and perform standard convolution operations on them only. We propose a simple and effective padding scheme --- interpolation-aware padding to pad a few empty voxels adjacent to the non-empty voxels and involving them in the CNN computation so that all neighboring voxels exist when computing point-wise features via the trilinear interpolation. For fine-grained 3D vision tasks where point-wise features are essential, like semantic segmentation and 3D detection, our network achieves higher prediction accuracy than the existing networks using the nearest neighbor interpolation or normalized trilinear interpolation with the zero-padding or the octree-padding scheme. Through extensive comparisons on various 3D segmentation and detection tasks, we demonstrate the superiority of 3D sparse CNNs with our sparse padding scheme in conjunction with feature interpolation.