Adaptive Geometry Reconstruction for Geometry-based Point Cloud Compression

Since the geometry constitutes most of the bitrate and is used for attribute coding, it is crucial for geometry-based point cloud compression (G-PCC). However, the current research focuses on geometry coding while ignoring reconstruction. In G-PCC, the reconstructed points are located at the center of the quantization nodes, which may not match the surface of the point clouds. Therefore, we first estimate the normal direction. Then, the offset direction of the reconstructed point is determined by considering its adjacent points’ occupancy and attributes. Finally, the position of the reconstructed point is adjusted taking into account both the normal and offset directions. The method aids in both objective and subjective quality. Experimental results demonstrate that the proposed method outperforms the state-of-the-art G-PCC. It has significant performance gains in point-to-point and point-to-plane errors, 4.5% and 9.0% on average, respectively. It also has a minor performance gain in attribute coding.