sjtuzq / point-radiance

[SIGGRAPH Asia 2022] Differentiable Point-Based Radiance Fields for Efficient View Synthesis

point-radiance

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This code release accompanies the following paper:

Differentiable Point-Based Radiance Fields for Efficient View Synthesis

Qiang Zhang, Seung-Hwan Baek, Szymon Rusinkiweicz, Felix Heide

Siggraph Asia, 2022

PDF | arXiv Abstract: We propose a differentiable rendering algorithm for efficient novel view synthesis. By departing from volume-based representations in favor of a learned point representation, we improve on existing methods more than an order of magnitude in memory and run- time, both in training and inference. The method begins with a uniformly-sampled random point cloud and learns per-point posi- tion and view-dependent appearance, using a differentiable splat- based renderer to train the model to reproduce a set of input train- ing images with the given pose. Our method is up to 300 × faster than NeRF in both training and inference, with only a marginal sacrifice in quality, while using less than 10 MB of memory for a static scene. For dynamic scenes, our method trains two orders of magnitude faster than STNeRF and renders at a near interactive rate, while maintaining high image quality and temporal coherence even without imposing any temporal-coherency regularizers.

Installation

We recommend using a conda environment for this codebase. The following commands will set up a new conda environment with the correct requirements:

# Create and activate new conda env
conda create -n my-conda-env python=3.9
conda activate my-conda-env

# Install pytorch and related libraries
conda install -y pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.0 -c pytorch
conda install numpy matplotlib tqdm imageio

Then follow the official INSTALL.md to install pytorch3d.

Reproduce

You can train the model on NeRF synthetic dataset within 3 minutes. Here datadir is the dataset folder path. Dataname is the scene name. Basedir is the log folder path. Data_r is the ratio between the used point number and the initialized point number. Splatting_r is the radius for the splatting.

python main.py --datadir xxx --dataname hotdog --basedir xxx --data_r 0.012 --splatting_r 0.015

After around three minutes, you can see the following output (the example is tested on one A100 GPU):

Training time: 148.59 s
Rendering quality: 34.70 dB
Rendering speed: 120.01 fps
Model size: 7.32 MB

Citation

If you find this work useful for your research, please consider citing:

@article{zhang2022differentiable,
  title={Differentiable Point-Based Radiance Fields for Efficient View Synthesis},
  author={Zhang, Qiang and Baek, Seung-Hwan and Rusinkiewicz, Szymon and Heide, Felix},
  journal={arXiv preprint arXiv:2205.14330},
  year={2022}
}