UVG-VPC：用于视觉体积视频编码的体素化点云数据集
UVG-VPC: Voxelized Point Cloud Dataset for Visual Volumetric Video-based Coding
AI总结：
本文发布UVG-VPC开放数据集，包含12个多样化点云视频序列及法线信息，旨在支持MPEG V3C点云压缩技术的开发与评估。
摘要：
点云压缩已成为沉浸式视觉媒体处理与流媒体传输中的关键因素。本文提出了一个名为UVG-VPC的新型开放数据集，用于MPEG视觉体积视频编码（V3C）技术的开发、评估和验证。该数据集根据其自身的非商业许可进行分发。它包含12个点云测试视频序列，这些序列在点的运动、RGB纹理、3D几何和表面遮挡方面具有多样化的特征。每个序列时长为10秒，包含以每秒25帧捕获的250帧。序列以9到12位的几何精度进行体素化，体素颜色属性表示为8位RGB值。该数据集还包含关联的法线，使其更适合评估点云压缩解决方案。发布UVG-VPC数据集的主要目标是促进V3C技术的发展，从而塑造该领域的未来。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
