Litcius/Paper detail

3D Point Cloud Attribute Compression Using Diffusion-Based Texture-Aware Intra Prediction

Yiting Shao, Xiaodong Yang, Wei Gao, Shan Liu, Ge Li

2024IEEE Transactions on Circuits and Systems for Video Technology37 citationsDOI

Abstract

There is an urgent need from various multimedia applications to efficiently compress point clouds. The Moving Picture Experts Group has released a standard platform called geometry-based point cloud compression (G-PCC). However, its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor (k-NN) based attribute prediction has limited efficiency for point clouds with rich texture and directional information. To overcome this problem, we propose a texture-aware attribute predictive coding framework in a point cloud diffusion model. In our work, attribute intra prediction is solved as a diffusion-based interpolation problem, and a general attribute predictor is developed. It is theoretically proven that G-PCC <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -NN based predictor is a degraded case of the proposed diffusion-based solution. First, a point cloud is represented as two levels of details with seeds as the inpainting mask and non-seed points to be predicted. Second, we design point cloud partial difference operators to perform energy-minimizing attribute inpainting from seeds to unknowns. Smooth attribute interpolation can be achieved via an iterative diffusion process, and an adaptive early termination is proposed to reduce complexity. Third, we propose a structure-adaptive attribute predictive coding scheme, where edge-enhancing anisotropic diffusion is employed to perform texture-aware attribute prediction. Finally, attributes of seeds are beforehand encoded and prediction residuals of left points are progressively encoded into bitstream. Experiments show the proposed scheme surpasses the state-of-the-art by an average of 14.14%, 17.52%, and 17.87% BD-BR gains on the coding of Y, U, and V components, respectively. Subjective results on attribute reconstruction quality also verify the advantage of our scheme.

Topics & Concepts

Computer scienceCompression (physics)Artificial intelligencePoint cloudTexture (cosmology)Data compressionCloud computingDiffusionComputer visionData miningPattern recognition (psychology)Image (mathematics)Materials scienceOperating systemComposite materialThermodynamicsPhysics3D Shape Modeling and AnalysisRemote Sensing and LiDAR ApplicationsComputer Graphics and Visualization Techniques