Transform aligned features improve point cloud attribute compression
Transform-Aligned Learned Features for Lossy Point Cloud Attribute Compression
Computer Vision and Pattern Recognition
Summary
Point clouds are 3D images made of points with colors or other details, which need to be compressed to save space. The paper shows a new way to process these details so the computer understands them better by aligning learned information with how the data is actually stored. This helps make the compression more efficient and keeps more of the important detail after decompression. The authors tested this method on several datasets and found it works better than older methods.
What this means in practice
- •For 3d graphics developers: Compress color and attribute data of 3D point clouds efficiently for applications like virtual reality and gaming.
- •For mapping and surveying teams: Reduce storage and transmission costs of detailed 3D scan data generated from land or structure surveys.
Authors
Yueru Chen, Pengpeng Yu, Dingquan Li, Wei Gao, Wei Zhang, Fei Song
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.