发表机构
Pengcheng Laboratory; Sun Yat-sen University; Peking University; Xidian University（鹏城实验室; 中山大学; 北京大学; 西安电子科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出变换对齐的学习特征（TALF），通过将属性变换应用于学习特征，显式对齐编码目标，在统一率失真目标下提升点云属性压缩性能。
AI 中文摘要
基于变换的方法通过将属性表示为变换系数，为点云属性压缩提供了有效框架。将学习到的空间上下文引入该框架需要将空间表示映射到变换域，但这种已知的基变换通常留给网络隐式学习。我们通过将属性变换应用于学习到的空间表示，提出变换对齐的学习特征（TALF），使其与编码目标显式对齐。我们的分析表明，所得特征精确表示平滑非线性模型的一阶预测项，且泰勒余项有界。我们将TALF集成到基于变换的属性编解码器中，在统一的系数域率失真目标下进行显式系数预测和条件残差熵建模，同时保留显式量化步长控制。在三个基准数据集和多种变换基上的大量实验表明，TALF相比传统和学习的基线方法提升了率失真性能。
英文摘要
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.
Comments19 pages
