Unbounded Neural Radiance Fields in Pytorch
Introduction
The UnboundedNeRFPytorch project aims to benchmark cutting-edge algorithms for large-scale radiance fields, often referred to as "unbounded NeRF". The terms "unbounded NeRF" and "large-scale NeRF" are used interchangeably in this context, as their underlying techniques are similar. Unlike some complex systems, this project focuses on providing a straightforward code repository that achieves state-of-the-art performance for unbounded NeRFs.
The project currently achieves impressive Peak Signal-to-Noise Ratio (PSNR) results across various benchmarks, surpassing existing methods such as NeRF++, Plenoxels, and DVGO on specific datasets like Unbounded Tanks & Temples and Mip-NeRF-360. Video examples of different scenes rendered by the project are provided to demonstrate its capabilities.
Project Development Updates
Named "UnboundedNeRFPytorch" to better reflect its scope, the project has seen several updates since its inception in July 2022. Noteworthy milestones include major code releases, the support of various NeRF techniques, and continuous improvement based on emerging research. The project also explores datasets such as the San Francisco Mission Bay dataset, offering optimized data for Pytorch usage.
Installation
For those interested in utilizing this project, the installation involves cloning the repository, setting up a conda environment, and installing the necessary libraries compatible with CUDA. Further steps involve building certain grid-based operators and, if desired, supporting additional libraries for custom scenes.
Using Unbounded NeRF with Public Datasets
The project extends support to several public datasets, facilitating training and result visualization. Users need to download processed datasets like Unbounded Tanks & Temples, Mip-NeRF-360, and San Francisco Mission Bay, and can follow a structured directory format for smooth integration. Instructions are available for running training models, rendering results, and executing specific benchmark tasks.
Building Custom Unbounded NeRF Worlds
Though the method is deprecated, instructions are provided for constructing custom unbounded NeRF scenes using the project. This involves organizing image data, employing COLMAP for scene reconstruction, and completing training cycles to visualize results.
Citations & Acknowledgements
The project builds upon extensive research, citing key works like DVGO and Block-NeRF, and acknowledges the contributions of other projects like DirectVoxGO and SVOX2. Details for citing the work underpinning the project and expressing gratitude towards contributors are provided.
Weekly Classified NeRF
The project maintains a weekly-updated classification of NeRF papers, providing comprehensive lists in both English and Chinese. Contributions and corrections are encouraged to ensure an updated repository of knowledge.
Contributors
The project is enriched by several contributors, each lending their expertise to enhance its development and performance.
This plain-language overview captures the essence and highlights of the UnboundedNeRFPytorch project, aiming to make its objectives, updates, and usage accessible to a broad audience interested in neural radiance fields.