Comments on "Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform"
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Abstract (arXiv)
The recently introduced coder based on region-adaptive hierarchical transform (RAHT) for the compression of point clouds attributes, was shown to have a performance competitive with the state-of-the-art, while being much less complex. In the paper "Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform", top performance was achieved using arithmetic coding (AC), while adaptive run-length Golomb-Rice (RLGR) coding was presented as a lower-performance lower-complexity alternative. However, we have found that by reordering the RAHT coefficients we can largely increase the runs of zeros and significantly increase the performance of the RLGR-based RAHT coder. As a result, the new coder, using ordered coefficients, was shown to outperform all other coders, including AC-based RAHT, at an even lower computational cost. We present new results and plots that should enhance those in the work of Queiroz and Chou to include the new results for RLGR-RAHT. We risk to say, based on the results herein, that RLGR-RAHT with sorted coefficients is the new state-of-the-art in point cloud compression.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 May 2018.
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Citing papers per year: 2021: 5; 2022: 4; 2023: 1.
10 papers.
- 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
- 2021VoxelContext-Net: An Octree based Framework for Point Cloud Compressioncited by 61
- 2022Neural Fourier Filter Bankcited by 29
- 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
- 2021Lossless Coding of Point Cloud Geometry using a Deep Generative Modelcited by 10
- 2022PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessmentcited by 8
- 2021Refining the bounding volumes for lossless compression of voxelized point clouds geometrycited by 2
- 2022Fractional Motion Estimation for Point Cloud Compressioncited by 2
- 20224DAC: Learning Attribute Compression for Dynamic Point Cloudscited by 2
- 20233D Gaussian Splatting for Real-Time Radiance Field Rendering8 Aug 2023
- 2022Neural Fourier Filter Bank4 Dec 2022
- 2022PCQA-GRAPHPOINT: Efficients Deep-Based Graph Metric For Point Cloud Quality Assessment4 Nov 2022
- 20224DAC: Learning Attribute Compression for Dynamic Point Clouds25 Apr 2022
- 2022Fractional Motion Estimation for Point Cloud Compression1 Feb 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
- 2021Lossless Coding of Point Cloud Geometry using a Deep Generative Model1 Jul 2021
- 2021Refining the bounding volumes for lossless compression of voxelized point clouds geometry1 Jun 2021
- 2021VoxelContext-Net: An Octree based Framework for Point Cloud Compression5 May 2021