EasyVolcap: Accelerating Neural Volumetric Video Research
This addresses the need for accessible tools to accelerate research and application development in volumetric video for fields like sports broadcasting and gaming, though it is incremental as it builds on existing neural scene representation methods.
The paper tackles the lack of a unified open-source library for neural volumetric video research by presenting EasyVolcap, a Python and PyTorch library that streamlines multi-view data processing, 4D scene reconstruction, and efficient dynamic rendering, with the source code made publicly available.
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.