Transform-Aligned Learned Features for Lossy Point Cloud Attribute Compression

2026-09-28

AI Summary

This study addresses the inefficiency in point cloud attribute compression caused by the implicit alignment between spatial features and transform-domain coding objectives. To overcome this limitation, we propose TALF, a method that explicitly maps learned spatial features into the transform domain for accurate coefficient prediction—a strategy theoretically proven equivalent to the first-order prediction term of a smooth nonlinear model. By integrating transform basis coding, deep learning-based feature extraction, and conditional residual entropy modeling, TALF achieves joint rate-distortion optimization in the coefficient domain. Experimental results demonstrate that TALF significantly outperforms existing traditional and learning-based baselines in rate-distortion performance across multiple benchmark datasets and under various transform bases.

Abstract

Transform-based methods provide an effective framework for point cloud attribute compression by representing attributes as transform coefficients. Introducing learned spatial context into this framework requires mapping spatial representations to the transform domain, but this known basis change is often left for the network to learn implicitly. We propose Transform-Aligned Learned Features (TALF) by applying the attribute transform to learned spatial representations, explicitly aligning them with the coding targets. Our analysis shows that the resulting features exactly represent the first-order prediction term of a smooth nonlinear model, with a bounded Taylor remainder. We integrate TALF into a transform-based attribute codec with explicit coefficient prediction and conditional residual entropy modeling under a unified coefficient-domain rate--distortion objective, while retaining explicit quantization-step control. Extensive experiments across three benchmark datasets and multiple transform bases demonstrate that TALF improves rate--distortion performance over conventional and learned baselines.

Problem

• point cloud attribute compression
• transform domain
• learned spatial context
• basis change alignment

Innovation

• Point Cloud Attribute Compression
• Transform-Aligned Learned Features
• Rate-Distortion Optimization
• Entropy Modeling
• Coefficient Prediction