LOSSY GEOMETRY COMPRESSION OF 3D POINT CLOUD DATA VIA AN ADAPTIVE OCTREE-GUIDED NETWORK

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

View graph of relations

Author(s)

  • Xuanzheng Wen
  • Xu Wang
  • Lin Ma
  • Yu Zhou
  • Jianmin Jiang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publication2020 IEEE International Conference on Multimedia and Expo (ICME)
PublisherIEEE
ISBN (Electronic)9781728113319
ISBN (Print)9781728113326
Publication statusPublished - Jul 2020

Publication series

Name
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Title2020 IEEE International Conference on Multimedia and Expo (ICME)
LocationVirtual
PlaceUnited Kingdom
CityLondon
Period6 - 10 July 2020

Abstract

In this paper, we propose a deep learning based framework for point cloud geometry lossy compression via hybrid representation of point cloud. First, the input raw 3D point cloud data is adaptively decomposed into non-overlapping local patches through adaptive Octree decomposition and clustering. Second, a framework of point cloud auto-encoder network with quantization layer is proposed for learning compact latent feature representation from each patch. Specifically, the proposed point cloud auto-encoder networks with different input size are trained for achieving optimal rate-distortion (RD) performance. Final, bitstream specifications of proposed compression systems with additional signaled meta-data and header information are designed to support parallel decoding and successive reconstruction. Experimental results shows that our proposed method can achieve 40.20% bitrate saving in average than the existing standard Geometry based Point Cloud Compression (G-PCC) codec.

Research Area(s)

  • 3D point cloud, auto-encoder, entropy estimation, geometry compression

Citation Format(s)

LOSSY GEOMETRY COMPRESSION OF 3D POINT CLOUD DATA VIA AN ADAPTIVE OCTREE-GUIDED NETWORK. / Wen, Xuanzheng; Wang, Xu; Hou, Junhui; Ma, Lin; Zhou, Yu; Jiang, Jianmin.

2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2020.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)