End-to-End Point Cloud Geometry Compression and Analysis with Sparse Tensor

With the rapid development of deep learning, encoded objects such as images, videos, and point cloud objects are increasingly used in downstream tasks optimized by deep learning. Traditional coding tools are optimized for human perception, not machine vision. Therefore, we bring forward a point cloud lossy compression method for machine vision, which uses elaborate extracted features to ensure the point cloud classification accuracy. We present a multi-scale channel attention module, which can well integrate features of various channels and dimensions, ensuring the compression performance and integrating the upper-level semantic information well. The experimental results demonstrates that our method achieves 30% BD-Rate gains and 5% improvement in classification compared with PCGCV2 in Modelnet40.