Motion estimation and filtered prediction for dynamic point cloud attribute compression

TL;DR

This paper introduces a novel block-based inter-coding scheme for point cloud attribute compression that combines integer-precision motion estimation with adaptive graph filtering, achieving significant coding improvements.

Contribution

It presents a new inter-coding method with motion estimation and graph-based filtering tailored for dynamic point cloud attribute compression.

Findings

Significant coding gain over existing methods

Effective noise reduction via adaptive graph filtering

Improved attribute prediction accuracy

Abstract

In point cloud compression, exploiting temporal redundancy for inter predictive coding is challenging because of the irregular geometry. This paper proposes an efficient block-based inter-coding scheme for color attribute compression. The scheme includes integer-precision motion estimation and an adaptive graph based in-loop filtering scheme for improved attribute prediction. The proposed block-based motion estimation scheme consists of an initial motion search that exploits geometric and color attributes, followed by a motion refinement that only minimizes color prediction error. To further improve color prediction, we propose a vertex-domain low-pass graph filtering scheme that can adaptively remove noise from predictors computed from motion estimation with different accuracy. Our experiments demonstrate significant coding gain over state-of-the-art coding methods.