BVI-CR：用于体积视频压缩的多视角人体数据集
BVI-CR: A Multi-View Human Dataset for Volumetric Video Compression
AI总结：
针对体积视频压缩缺乏高质量数据集的问题，提出包含18个多视角RGB-D人体动作序列的BVI-CR数据集，并基准测试表明神经表示方法相比传统编码平均PSNR增益达38%。
摘要：
沉浸式技术和三维重建的进步使得创建具有精细细节的真实世界物体和环境的数字复制品成为可能。这些过程产生了大量的三维数据，需要更高效的压缩方法来满足与数据存储和传输相关的内存和带宽限制。然而，高效三维数据压缩方法的开发和验证受到缺乏全面且高质量的体积视频数据集的限制，与二维图像和视频数据库相比，获取这些数据集通常需要更多的努力并消耗更多的资源。为了弥补这一差距，我们提出了一个开放的多视角体积人体数据集，命名为BVI-CR，其中包含18个多视角RGB-D捕获及其对应的带纹理的多边形网格，描绘了各种不同的人体动作。每个视频序列包含10个视角，分辨率为1080p，时长为10至15秒，帧率为30FPS。使用BVI-CR，我们按照MPEG MIV通用测试条件，对三种传统的和基于神经坐标的多视角视频压缩方法进行了基准测试，并基于各种质量指标报告了它们的率失真性能。结果表明，与传统视频编码方法相比，基于神经表示的方法在体积视频压缩方面具有巨大潜力（在PSNR上平均编码增益高达38%）。该数据集为体积重建、压缩和质量评估等多种任务提供了一个开发和验证平台。该数据库将在https://github.com/fan-aaron-zhang/bvi-cr 公开共享。
英文摘要：
The advances in immersive technologies and 3D reconstruction have enabled the creation of digital replicas of real-world objects and environments with fine details. These processes generate vast amounts of 3D data, requiring more efficient compression methods to satisfy the memory and bandwidth constraints associated with data storage and transmission. However, the development and validation of efficient 3D data compression methods are constrained by the lack of comprehensive and high-quality volumetric video datasets, which typically require much more effort to acquire and consume increased resources compared to 2D image and video databases. To bridge this gap, we present an open multi-view volumetric human dataset, denoted BVI-CR, which contains 18 multi-view RGB-D captures and their corresponding textured polygonal meshes, depicting a range of diverse human actions. Each video sequence contains 10 views in 1080p resolution with durations between 10-15 seconds at 30FPS. Using BVI-CR, we benchmarked three conventional and neural coordinate-based multi-view video compression methods, following the MPEG MIV Common Test Conditions, and reported their rate quality performance based on various quality metrics. The results show the great potential of neural representation based methods in volumetric video compression compared to conventional video coding methods (with an up to 38\% average coding gain in PSNR). This dataset provides a development and validation platform for a variety of tasks including volumetric reconstruction, compression, and quality assessment. The database will be shared publicly at \url{https://github.com/fan-aaron-zhang/bvi-cr}.
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。
