Exploring Structural Sparsity in Neural Image Compression

With the rapid development of 3D sensing and reconstruction technologies, point cloud compression has become an active research area. When designing compression methods for point cloud attributes, the reconstructed geometry information is commonly used to guide the attribute compression. Specifically, the geometric distance between points is widely applied in different forms to assist attribute prediction. However, due to the complicated spatial distributions of 3D point clouds, using distance as the sole feature may not be optimal. in this paper, the spatial distribution of point clouds is further considered to derive a distribution-driven attribute prediction. Experimental results show that the proposed method can significantly improve the coding efficiency of G-PCC. The proposed method has been adopted in the technologies under consideration (TuC) for MPEG G-PCC and will be included in the second version of the G-PCC specification.