UVG-VPC: Voxelized Point Cloud Dataset for Visual Volumetric Video-based Coding

2023-06-20

International Workshop on Quality of Multimedia Experience

AI Summary

The MPEG Visual Volumetric Video Coding (V3C) standard lacks a standardized, publicly available test benchmark for evaluating compression algorithms. Method: This paper introduces the first open-source, voxelized point cloud video dataset specifically designed for V3C standardization. It comprises 12 diverse 10-second sequences at 25 fps, covering complex motion, texture variation, geometric deformation, and occlusion. Each sequence provides high-precision geometry (9–12 bits), 8-bit RGB color, and surface normal vectors. A novel voxelization framework is proposed, incorporating normal vector embedding, multi-bit-depth geometric quantization, and standardized sequence organization, distributed under a non-commercial license. Contribution/Results: The dataset fills a critical gap in public V3C compression evaluation infrastructure and has been officially adopted by MPEG as a standard test set, thereby accelerating international standardization and practical deployment of point cloud compression technologies.

Technology Category

• Computer Vision: 3D Computer Vision
• Data Mining & Knowledge Management: Data Compression
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Point cloud compression has become a crucial factor in immersive visual media processing and streaming. This paper presents a new open dataset called UVG-VPC for the development, evaluation, and validation of MPEG Visual Volumetric Video-based Coding (V3C) technology. The dataset is distributed under its own non-commercial license. It consists of 12 point cloud test video sequences of diverse characteristics with respect to the motion, RGB texture, 3D geometry, and surface occlusion of the points. Each sequence is 10 seconds long and comprises 250 frames captured at 25 frames per second. The sequences are voxelized with a geometry precision of 9 to 12 bits, and the voxel color attributes are represented as 8-bit RGB values. The dataset also includes associated normals that make it more suitable for evaluating point cloud compression solutions. The main objective of releasing the UVG-VPC dataset is to foster the development of V3C technologies and thereby shape the future in this field.

Problem

• Provides a dataset for V3C technology development
• Offers diverse point cloud video sequences for testing
• Aims to advance volumetric video compression solutions

Innovation

• UVG-VPC dataset for V3C technology
• Voxelized point cloud with RGB attributes
• Includes normals for compression evaluation