1st International Workshop on Advances in Point Cloud Compression, Processing and Analysis


Workshop at ACM Multimedia 2022



Overview

Point clouds are attracting much attention from academia, industry and standardization organizations such as MPEG, JPEG, and AVS. 3D Point clouds consisting of thousands or even millions of points with attributes can represent real-world objects and scenes in a way that enables an improved immersive visual experience and facilitates complex 3D vision tasks. In addition to various point cloud analysis and processing tasks (e.g., segmentation, classification, 3D object detection, registration), efficient compression for these large-scale 3D visual data is essential to make point cloud applications more effective. This workshop focuses on point cloud processing, analysis, and compression in challenging situations to further improve visual experience and machine vision performance. Both learning-based and non-learning-based perception-oriented optimization algorithms for compression and processing are solicited. Contributions that advance the state-of-the-art in analysis tasks, are also welcomed.

Announcements

  • 10/1 - The schedule of this workshop is released. new
  • 8/29 - Eight high quality papers are accepted. new
  • 4/11 - CfP is released. new
  • 4/11 - Workshop official website is now avaliable. new

Call for Papers

This workshop intends to provide a forum for researchers and engineers to present their latest innovations and share their experiences on all aspects of algorithm design and implementation of new point cloud compression and processing techniques, as well as the advancements in standardization activities. Topics of interest include, but are not limited to:
  • Geometry-based and Video-based point cloud compression (G-PCC and V-PCC)
  • Learning-based point cloud compression
  • Point cloud compression and processing for human visual experience and machine vision tasks
  • Quality assessment and perception models for degraded point clouds
  • Coding tools and optimization in point cloud compression standards
  • Point cloud processing tasks (e.g., denoising, upsampling, compression artifact removal)
  • Classification, segmentation, 3D object detection, tracking, retrieval, registration, and other analysis tasks for point clouds
  • Applications and system implementations of point clouds
Important dates:
  • Call for papers: 8th April, 2022
  • Paper submission deadline (Abstract Registration & Final Manuscript): Jul 11, 2022 11:59PM UTC-0 (Jul 12, 2022, 08:29AM GMT+8)
  • Paper notification: 29th July, 2022
  • Paper camera-ready: 21st August, 2022

Paper Submission

Format: Submitted papers (.pdf format) must use the ACM Article Template https://www.acm.org/publications/proceedings-template. Please remember to add Concepts and Keywords.
Length: Submissions can be of varying length from 4 to 8 pages, plus additional pages for the reference pages; i.e., the reference page(s) are not counted to the page limit of 4 to 8 pages. There is no distinction between long and short papers, but the authors may themselves decide on the appropriate length of the paper. All papers will undergo the same review process and review period.
Reviewing Process: Paper submissions must conform with the “double-blind” review policy. All papers will be peer-reviewed by experts in the field. Acceptance will be based on relevance to the workshop, scientific novelty, and technical quality. The workshop papers will be published in the ACM Digital Library.
Submission Site: https://openreview.net/group?id=acmmm.org/ACMMM/2022/Workshop/APCCPA.

Workshop Schedule

Our workshop will be held online.

Time (October 14, Local Portugal Time) Paper Title
14:00 PM - 14:15 PM IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression
14:15 PM - 14:30 PM GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression
14:30 PM - 14:45 PM Wiener Filter-Based Point Cloud Adaptive Denoising for Video-based Point Cloud Compression
14:45 PM - 15:00 PM OpenPointCloud-V2: A Deep Learning Based Open-Source Algorithm Library of Point Cloud Processing
15:00 PM - 15:15 PM End-to-End Point Cloud Geometry Compression and Analysis with Sparse Tensor
15:15 PM - 15:30 PM Transformer and Upsampling-Based Point Cloud Compression
15:30 PM - 15:45 PM Quality Evaluation of Machine Learning-based Point Cloud Coding Solutions
15:45 PM - 16:00 PM View-Adaptive Streaming of Point Cloud Scenes through Combined Decomposition and Video-based Coding

Organizers

Wei Gao

Peking University
Ge Li

Peking University
Hui Yuan

Shandong University
Raouf Hamzaoui

De Montfort University
Zhu Li

University of Missouri - Kansas City
Shan Liu

Tencent