发布用于3D点云压缩的新型体素化视频数据集
UVG-VPC: Voxelized Point Cloud Dataset for Visual Volumetric Video-based Coding
- 构建12个多样化点云视频序列，每秒25帧，共250帧
- 体素化精度9-12位，颜色用8位RGB表示，含法向量信息
- 专为MPEG V3C技术评估设计，适合点云压缩研究者使用
点云压缩已成为沉浸式视觉媒体处理与流传输的关键。本文发布一个名为UVG-VPC的新开源数据集，用于MPEG视觉体素视频编码（V3C）技术的开发、评估与验证。该数据集采用非商业许可发布，包含12个具有不同运动特征、RGB纹理、三维几何结构及表面遮挡特性的点云视频序列，每个序列时长10秒，共250帧，采样率为25帧/秒。所有序列经体素化处理，几何精度为9至12位，体素颜色属性以8位RGB表示，并附带法向量信息，有助于更全面评估点云压缩方案。发布该数据集的主要目的是推动V3C技术发展，引领未来方向。
原文摘要 · Abstract (English)
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.
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