mlsanityData to 14 Oct 2026

Efficient dynamic point cloud coding using Slice-Wise Segmentation

arXiv 2208.08061 ↗First posted 17 Aug 2022cs.CV cs.MM eess.IVcited by 0

Not on the map. Why?

Abstract (arXiv)

With the fast growth of immersive video sequences, achieving seamless and high-quality compressed 3D content is even more critical. MPEG recently developed a video-based point cloud compression (V-PCC) standard for dynamic point cloud coding. However, reconstructed point clouds using V-PCC suffer from different artifacts, including losing data during pre-processing before applying existing video coding techniques, e.g., High-Efficiency Video Coding (HEVC). Patch generations and self-occluded points in the 3D to the 2D projection are the main reasons for missing data using V-PCC. This paper proposes a new method that introduces overlapping slicing as an alternative to patch generation to decrease the number of patches generated and the amount of data lost. In the proposed method, the entire point cloud has been cross-sectioned into variable-sized slices based on the number of self-occluded points so that data loss can be minimized in the patch generation process and projection. For this, a variable number of layers are considered, partially overlapped to retain the self-occluded points. The proposed method's added advantage is to reduce the bits requirement and to encode geometric data using the slicing base position. The experimental results show that the proposed method is much more flexible than the standard V-PCC method, improves the rate-distortion performance, and decreases the data loss significantly compared to the standard V-PCC method.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 37 references that are in the map. First posted on arXiv 17 Aug 2022.

Builds on

4 of 37 references.

References · 4

  1. 2021All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agendacited by 67
  2. 2021VoxelContext-Net: An Octree based Framework for Point Cloud Compressioncited by 61
  3. 2019A Comprehensive Study and Comparison of Core Technologies for MPEG 3D Point Cloud Compressioncited by 19
  4. 2021Video-based Point Cloud Compression Artifact Removalcited by 4

Built on by

Not cited yet.