Outlier grouping based point cloud compression

Abstract

A Folding-based point cloud compression system or a regular convolution-based point cloud compression system may lack the ability to precisely manage compression of all points in a point cloud frame. For example, they may provide good predicted positions for some points but not necessarily an exact reconstruction, especially after being quantized. Consequently, some outlier points may fall outside of the surface constructed by a Folding-based method or a convolution-based method. In one implementation, we propose to compress two types of outlier points separate from 'inlier' points. First, we detect model outlier points, which are points that do not have good prediction from a model. Then quantization outlier point detection is conducted to further separate those points that are sensitive to the compression (e.g., quantization) in the model transformed domain. Such outlier processing would provide efficient coding of most points (inlier points) and special handling on the outlier points.