EasyVolcap：加速神经体素视频研究
EasyVolcap: Accelerating Neural Volumetric Video Research
AI总结：
本文提出开源 Python 与 PyTorch 库 EasyVolcap，统一多视图数据处理、4D场景重建和动态体积视频渲染，以加速神经体积视频研究与应用开发。
摘要：
体积视频是一种对艺术表演、体育赛事和远程对话等动态事件进行数字记录的技术。采集完成后，这类体积摄影作品可在平面屏幕、3D显示器或VR头显上从任意视点和时间戳观看，从而在体育转播、视频会议、游戏和电影制作等多种应用中实现沉浸式观看体验和更灵活的内容创作。随着用于体积视频的神经场景表示近期取得进展并引发快速增长的兴趣，迫切需要一个统一的开源库，以简化体积视频捕获、重建和渲染流程，使研究人员和非专业用户都能够开发这一新兴技术的各类算法与应用。本文提出 EasyVolcap，这是一个用于加速神经体积视频研究的 Python 与 PyTorch 库，目标是统一多视图数据处理、4D场景重建以及高效动态体积视频渲染的流程。源代码可在 https://github.com/zju3dv/EasyVolcap 获取。
英文摘要：
Volumetric video is a technology that digitally records dynamic events such as artistic performances, sporting events, and remote conversations. When acquired, such volumography can be viewed from any viewpoint and timestamp on flat screens, 3D displays, or VR headsets, enabling immersive viewing experiences and more flexible content creation in a variety of applications such as sports broadcasting, video conferencing, gaming, and movie productions. With the recent advances and fast-growing interest in neural scene representations for volumetric video, there is an urgent need for a unified open-source library to streamline the process of volumetric video capturing, reconstruction, and rendering for both researchers and non-professional users to develop various algorithms and applications of this emerging technology. In this paper, we present EasyVolcap, a Python & Pytorch library for accelerating neural volumetric video research with the goal of unifying the process of multi-view data processing, 4D scene reconstruction, and efficient dynamic volumetric video rendering. Our source code is available at https://github.com/zju3dv/EasyVolcap.
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。
