Denoising and Inpainting for Point Clouds Compressed by V-PCC
Keming Cao, Pamela C. Cosman
Abstract
With the development of immersive video, the quality of compressed 3D content has become an important issue. Video-based Point Cloud Compression (V-PCC) is a popular compression method for point cloud sequences; it achieves the highest quality among MPEG proposals. Compressed point clouds suffer from various artifacts when a high quantization parameter (QP) is used. Examining the causes and types of V-PCC artifacts that occur, we propose a framework to remove the highly noticeable outlier and crack artifacts caused by V-PCC so as to improve compressed point cloud visual quality. A subjective experiment showed that our approach provides significantly improved visual quality, and the improvement becomes more obvious with increasing QP values. Objective evaluation with point-to-point Mean Squared Error (p2p-MSE) shows our proposed method can improve point cloud quality and provides competitive results with lower complexity compared with other methods for point cloud outlier removal and inpainting.