When Dynamic Neural Network Meets Point Cloud Compression: Computation-Aware Variable Rate and Checkerboard Context

For exploring the Rate-Distortion-Complexity (RDC) optimization in point cloud compression, we propose a point cloud compressor with dynamic channel. In the transform process of the proposed compressor ( Fig 1.a ), we devise a sparse convolution operator, named AdaSConv, shown in Fig 1.b , to support RDC optimization, which ensures model capacity can adjust Rate-Distortion performance. What is more, to fill the blank of improved entropy model in point cloud feature compression, we design a 3D checkerboard entropy model. The 3D checkerboard divides points in the whole space into two parts: anchor and non-anchor, which will be compressed in sequence. As Fig 1.c illustrates, the compression of non-anchor will refer to the information in coded anchor points through Masked AdaSConv ( Fig 1.d ). We conduct floating point operations (FLOPs) computation, which reveals that the smallest rate point only consumes 7% of the FLOPs used by the full-width model. Besides, experiment results show 3D checkerboard has at most 13.86% gains of BD-Rate compared with factorized entropy model in the same experimental settings in Owill dataset with only slight extra time and computation.