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.

翻译：体积视频是一种数字记录动态事件（如艺术表演、体育赛事和远程对话）的技术。获取此类体视图像后，可在平面屏幕、3D显示器或VR头显上从任意视角和时间戳进行观看，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。随着神经场景表征在体积视频领域的近期进展和快速增长的关注度，亟需一个统一的开源库来简化体积视频的采集、重建和渲染流程，以便研究人员和非专业用户开发这一新兴技术的各种算法与应用。本文提出EasyVolcap——一个基于Python和Pytorch的库，旨在通过统一多视图数据处理、4D场景重建和高效动态体积视频渲染的过程来加速神经体积视频研究。我们的源代码可在https://github.com/zju3dv/EasyVolcap获取。