Dynamic Polygon Clouds: Representation and Compression for VR/AR
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Abstract (arXiv)
We introduce the em polygon cloud, also known as a polygon set or em soup, as a compressible representation of 3D geometry (including its attributes, such as color texture) intermediate between polygonal meshes and point clouds. Dynamic or time-varying polygon clouds, like dynamic polygonal meshes and dynamic point clouds, can take advantage of temporal redundancy for compression, if certain challenges are addressed. In this paper, we propose methods for compressing both static and dynamic polygon clouds, specifically triangle clouds. We compare triangle clouds to both triangle meshes and point clouds in terms of compression, for live captured dynamic colored geometry. We find that triangle clouds can be compressed nearly as well as triangle meshes, while being far more robust to noise and other structures typically found in live captures, which violate the assumption of a smooth surface manifold, such as lines, points, and ragged boundaries. We also find that triangle clouds can be used to compress point clouds with significantly better performance than previously demonstrated point cloud compression methods. In particular, for intra-frame coding of geometry, our method improves upon octree-based intra-frame coding by a factor of 5-10 in bit rate. Inter-frame coding improves this by another factor of 2-5. Overall, our dynamic triangle cloud compression improves over the previous state-of-the-art in dynamic point cloud compression by 33% or more.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Oct 2016.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
5 papers.
- 2021LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networkscited by 19
- 2021Attribute Artifacts Removal for Geometry-based Point Cloud Compressioncited by 13
- 2022PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessmentcited by 8
- 2022Motion estimation and filtered prediction for dynamic point cloud attribute compressioncited by 1
- 2026GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Cloudscited by 0
- 2026GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Clouds4 Jun 2026
- 2022PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessment4 Nov 2022
- 2022Motion estimation and filtered prediction for dynamic point cloud attribute compression15 Oct 2022
- 2021Attribute Artifacts Removal for Geometry-based Point Cloud Compression1 Dec 2021
- 2021LVAC: Learned Volumetric Attribute Compression for Point Clouds using Coordinate Based Networks17 Nov 2021