Attribute Artifacts Removal for Geometry-Based Point Cloud Compression

Fumos: Neural Compression and Progressive Refinement for Continuous Point Cloud Video Streaming

Point cloud video (PCV) offers watching experiences in photorealistic 3D scenes with six-degree-of-freedom (6-DoF), enabling a variety of VR and AR applications. The user's Field of View (FoV) is more fickle with 6-DoF movement than 3-DoF movement in 360-degree video. PCV streaming is extremely bandwidth-intensive. However, current streaming systems require hundreds of Mbps bandwidth, exceeding the bandwidth capabilities of commodity devices. To save bandwidth, FoV-adaptive streaming predicts a user's FoV and only downloads point cloud data falling in the predicted FoV. But it is difficult to accurately predict the user's FoV even 2-3 seconds before playback due to 6-DoF. Misprediction of FoV or network bandwidth dips results in frequent stalls. To avoid rebuffering, existing systems would cause incomplete FoV and degraded experience, deteriorating the user's quality of experience (QoE). In this paper, we describe Fumos, a novel system that preserves interactive experience by avoiding playback stalls while maintaining high perceptual quality and high compression rate. We find a research gap in inter-frame redundant utilization and progressive mechaism. Fumos has three crucial designs, including (1) Neural compression framework with inter-frame coding, namely N-PCC, which achieves both bandwidth efficiency and high fidelity. (2) Progressive refinement streaming framework that enables continuous playback by incrementally upgrading a fetched portion to a higher quality (3) System-level adaptation that employs Lyapunov optimization to jointly optimize the long-term user QoE. Experimental results demonstrate that Fumos significantly outperforms Draco, achieving an average decoding rate acceleration of over 260×. Moreover, the proposed compression framework N-PCC attains remarkable BD-Rate gains, averaging 91.7% and 51.7% against the state-of-the-art point cloud compression methods G-PCC and V-PCC, respectively.

GQE-Net: A Graph-Based Quality Enhancement Network for Point Cloud Color Attribute

In recent years, point clouds have become increasingly popular for representing three-dimensional (3D) visual objects and scenes. To efficiently store and transmit point clouds, compression methods have been developed, but they often result in a degradation of quality. To reduce color distortion in point clouds, we propose a graph-based quality enhancement network (GQE-Net) that uses geometry information as an auxiliary input and graph convolution blocks to extract local features efficiently. Specifically, we use a parallel-serial graph attention module with a multi-head graph attention mechanism to focus on important points or features and help them fuse together. Additionally, we design a feature refinement module that takes into account the normals and geometry distance between points. To work within the limitations of GPU memory capacity, the distorted point cloud is divided into overlap-allowed 3D patches, which are sent to GQE-Net for quality enhancement. To account for differences in data distribution among different color components, three models are trained for the three color components. Experimental results show that our method achieves state-of-the-art performance. For example, when implementing GQE-Net on a recent test model of the geometry-based point cloud compression (G-PCC) standard, 0.43 dB, 0.25 dB and 0.36 dB Bjφntegaard delta (BD)-peak-signal-to-noise ratio (PSNR), corresponding to 14.0%, 9.3% and 14.5% BD-rate savings were achieved on dense point clouds for the Y, Cb, and Cr components, respectively. The source code of our method is available at https://github.com/xjr998/GQE-Net.