Point Cloud Compression

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1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Transform-Aligned Learned Features for Lossy Point Cloud Attribute Compression
Sep 28, 2026Yueru Chen, Pengpeng Yu, Dingquan Li +3Point Cloud Compression
Pengcheng Laboratory, Shenzhen, China · Sun Yat-sen University, Shenzhen, China · Peking University, Shenzhen, China +1
CC-4DGS: Computational Deformation and Point-Cloud Compression for Storage-Efficient Dynamic Gaussian Splatting
Sep 2, 2026Kyungdae Park, Chae Eun RheeGaussian Splatting Dynamic Novel View Synthesis
Department of Artificial Intelligence Semiconductor Engineering, Hanyang University, Seoul 04763, South Korea · Department of Electronic Engineering, Hanyang University, Seoul 04763, South Korea
Geometry-Based Compression of Plenoptic Point Clouds
Aug 11, 2026Davi R. Freitas, Gustavo L. Sandri, Ricardo L. de QueirozPoint Cloud Compression
Inria - Rennes Bretagne-Atlantique Rennes, France · Instituto Federal de Bras´ılia Bras´ılia, Brazil · Universidade de Bras´ılia Bras´ılia, Brazil
Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression
Aug 1, 2026Yihan Qiu, Xiaodong Lin, Baoquan Zhao +2Point Cloud Compression
Peking University · Sun Yat-sen University
Motion Estimation Techniques for Volumetric Video Attribute Compression
Jul 3, 2026Haoran Hong, Eduardo Pavez, Antonio Ortega +2Point Cloud Compression
University of Southern California, 3740 McClintock Ave., Los Angeles, CA 90089-2564, USA · KDDI Research, Inc., 2-1-15 Ohara, Fujimino, Saitama, 356-8502, Japan
GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Clouds
Jun 4, 2026Rajrup Ghosh, Haodong Wang, Haoran Hong +63DGS Compression 3D Gaussian Splatting
University of Southern California
Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compression
May 18, 2026Chang Sun, Hui Yuan, Shiqi Jiang +3Point Cloud Compression Predictive Coding
School of Control Science and Engineering, Shandong University, Jinan 250061, China · Key Laboratory of Machine Intelligence and System Control, Ministry of Education, Jinan 250061, China · School of Computer Science and Technology, Shandong University, Qingdao, 266237, China +1
PACE: Post-Causal Entropy Modeling for Learned LiDAR Point Cloud Compression
May 2, 2026Jiahao Zhu, Kang You, Dandan Ding +1Point Cloud Compression Airborne Laser Scanning
School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China. · School of Electronic Science and Engineering, Nanjing University, Nanjing, China.
TAFA-GSGC: Group-wise Scalable Point Cloud Geometry Compression with Progressive Residual Refinement
Apr 30, 2026Xiumei Li, Alexander Kopte, André KaupPoint Cloud Compression Rate-Distortion Optimization
Chair of Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen, Germany
Towards Practical Lossless Neural Compression for LiDAR Point Clouds
Mar 26, 2026Pengpeng Yu, Haoran Li, Runqing Jiang +4Point Cloud Compression
Sun Yat-sen University, China · Pengcheng Laboratory, China
Deep Unrolling of Sparsity-Induced RDO for 3D Point Cloud Attribute Coding
Sep 10, 2025Tam Thuc Do, Philip A. Chou, Gene CheungPoint Cloud Compression Rate-Distortion Optimization
department of EECS, York University, 4700 Keele Street, Toronto, M3J 1P3, Canada
Squeeze3D: Extreme Neural Compression with Latent Space Bridging
Jun 9, 2025Rishit Dagli, Yushi Guan, Sankeerth Durvasula +2Point Cloud Compression
University of Toronto
Efficient LiDAR Reflectance Compression via Scanning Serialization
May 14, 2025Jiahao Zhu, Kang You, Dandan Ding +1Point Cloud Compression
School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China · School of Electronic Science and Engineering, Nanjing University, Nanjing, China

Sep 28, 2026, cs.CVJ/K move · Enter open · S save

Transform-Aligned Learned Features for Lossy Point Cloud Attribute Compression

Yueru Chen, Pengpeng Yu, Dingquan Li, Wei Gao+2

Pengcheng Laboratory, Shenzhen, China · Sun Yat-sen University, Shenzhen, China · Peking University, Shenzhen, China+1

Point Cloud Compression

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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.

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