Neural Sparse Voxel Fields (NSVF) provides an open-source implementation for fast and high-quality free-viewpoint rendering of real-world scenes. It addresses limitations in existing neural rendering approaches, such as blurry outputs and slow rendering processes, by introducing a novel neural scene representation. This project is targeted at researchers and practitioners in computer vision and graphics interested in advanced neural rendering techniques.

How It Works

NSVF utilizes a sparse voxel field representation combined with deep neural networks. The core idea is to implicitly learn scene geometry and appearance from 2D observations. The sparse voxel structure allows for efficient ray-voxel intersection tests, and the network learns to predict color and density within these voxels. This approach enables faster rendering compared to dense volumetric methods and can achieve higher quality by effectively capturing fine details.

Quick Start & Requirements

• Install: pip install -r requirements.txt followed by pip install --editable ./ or python setup.py build_ext --inplace.
• Prerequisites: Python 3.7, PyTorch 1.4.0, Nvidia GPU (Tesla V100 32GB recommended), CUDA 10.1. Nvidia apex library is optional.
• Resources: Training requires significant GPU memory and time. Datasets are provided for download.
• Links: Project Page, Paper, Video

Highlighted Details

• Implements the NSVF method from the NeurIPS 2020 Spotlight paper.
• Includes an unofficial implementation for NeRF.
• Supports training, evaluation, free-viewpoint rendering, and geometry extraction (marching cubes).
• Offers options for octree acceleration, adaptive voxel size reduction, and self-pruning for efficiency.

Maintenance & Community

The project is from Facebook AI Research (FAIR). No specific community links (Discord/Slack) or roadmap are provided in the README.

Licensing & Compatibility

• License: MIT License.
• Compatibility: Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The code is implemented in PyTorch 1.4.0 and requires specific older versions of CUDA and Python, which may pose compatibility challenges with current environments. Only GPU-based learning and rendering are supported.