AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radius

SIGGRAPH Asia 2024

Project Page | Paper | Supplementary Material

AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radius
Xinzhe Wang^*, Ran Yi^*, Lizhuang Ma^†
^*Equal contribution. ^†Corresponding author.

Official implementation of the paper "AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radius"

📺 Fast Forward

fast-forward-light.mp4

3D Gaussian Splatting enables real-time rendering of complex scenes but still remains unnecessary overhead. We propose AdR-Gaussian, which employs lossless early culling to narrow the tile range of each Gaussian, and proposes a load balancing method to minimize thread waiting time, achieving significant acceleration in rendering speed.

🔭Overview

The codebase is built off of the codebase of 3D Gaussian Splatting, using PyTorch and CUDA extensions in a Python environment to produce trained models with similar requirements. You can easily get started using the commands of the seminal project.

📕Cloning the Repository

The repository contains submodules.

# SSH
git clone git@github.com:hiroxzwang/adrgaussian.git --recursive

# HTTPS
git clone https://github.com/hiroxzwang/adrgaussian.git --recursive

🚀Local Setup

Our default, provided install method is based on Conda package and environment management:

SET DISTUTILS_USE_SDK=1 # Windows only
conda env create --file environment.yml
conda activate adr_gaussian

👈Training

To run the optimizer, simply use

python train.py -s <path to COLMAP or NeRF Synthetic dataset> -m <path to save model>

Command Line Arguments for train.py

--source_path / -s

Path to the source directory containing a COLMAP or Synthetic NeRF data set.

--model_path / -m

Path where the trained model should be stored (output/<random> by default).

--images / -i

Alternative subdirectory for COLMAP images (images by default).

--eval

Add this flag to use a MipNeRF360-style training/test split for evaluation.

--resolution / -r

Specifies resolution of the loaded images before training. If provided 1, 2, 4 or 8, uses original, 1/2, 1/4 or 1/8 resolution, respectively. For all other values, rescales the width to the given number while maintaining image aspect. If not set and input image width exceeds 1.6K pixels, inputs are automatically rescaled to this target.

--data_device

Specifies where to put the source image data, cuda by default, recommended to use cpu if training on large/high-resolution dataset, will reduce VRAM consumption, but slightly slow down training. Thanks to HrsPythonix.

--white_background / -w

Add this flag to use white background instead of black (default), e.g., for evaluation of NeRF Synthetic dataset.

--sh_degree

Order of spherical harmonics to be used (no larger than 3). 3 by default.

--convert_SHs_python

Flag to make pipeline compute forward and backward of SHs with PyTorch instead of ours.

--convert_cov3D_python

Flag to make pipeline compute forward and backward of the 3D covariance with PyTorch instead of ours.

--debug

Enables debug mode if you experience erros. If the rasterizer fails, a dump file is created that you may forward to us in an issue so we can take a look.

--debug_from

Debugging is slow. You may specify an iteration (starting from 0) after which the above debugging becomes active.

--iterations

Number of total iterations to train for, 30_000 by default.

--ip

IP to start GUI server on, 127.0.0.1 by default.

--port

Port to use for GUI server, 6009 by default.

--test_iterations

Space-separated iterations at which the training script computes L1 and PSNR over test set, 7000 30000 by default.

--save_iterations

Space-separated iterations at which the training script saves the Gaussian model, 7000 30000 <iterations> by default.

--checkpoint_iterations

Space-separated iterations at which to store a checkpoint for continuing later, saved in the model directory.

--start_checkpoint

Path to a saved checkpoint to continue training from.

--quiet

Flag to omit any text written to standard out pipe.

--feature_lr

Spherical harmonics features learning rate, 0.0025 by default.

--opacity_lr

Opacity learning rate, 0.05 by default.

--scaling_lr

Scaling learning rate, 0.005 by default.

--rotation_lr

Rotation learning rate, 0.001 by default.

--position_lr_max_steps

Number of steps (from 0) where position learning rate goes from initial to final. 30_000 by default.

--position_lr_init

Initial 3D position learning rate, 0.00016 by default.

--position_lr_final

Final 3D position learning rate, 0.0000016 by default.

--position_lr_delay_mult

Position learning rate multiplier (cf. Plenoxels), 0.01 by default.

--densify_from_iter

Iteration where densification starts, 500 by default.

--densify_until_iter

Iteration where densification stops, 15_000 by default.

--densify_grad_threshold

Limit that decides if points should be densified based on 2D position gradient, 0.0002 by default.

--densification_interval

How frequently to densify, 100 (every 100 iterations) by default.

--opacity_reset_interval

How frequently to reset opacity, 3_000 by default.

--lambda_dssim

Influence of SSIM on total loss from 0 to 1, 0.2 by default.

--percent_dense

Percentage of scene extent (0--1) a point must exceed to be forcibly densified, 0.01 by default.

🔍Evaluation

By default, the trained models use all available images in the dataset. To train them while withholding a test set for evaluation, use the --eval flag. This way, you can render training/test sets and produce error metrics as follows:

python train.py -s <path to COLMAP or NeRF Synthetic dataset> --eval -m <path to save the trained model> # Train with train/test split
python render.py -m <path to trained model> # Generate renderings
python metrics.py -m <path to trained model> # Compute error metrics on renderings

Command Line Arguments for render.py

--model_path / -m

Path to the trained model directory you want to create renderings for.

--skip_train

Flag to skip rendering the training set.

--skip_test

Flag to skip rendering the test set.

--measure_fps

Flag to measure the rendering speed.

--skip_render

Flag to skip writing the image files.

--quiet

Flag to omit any text written to standard out pipe.

The below parameters will be read automatically from the model path, based on what was used for training. However, you may override them by providing them explicitly on the command line.

--source_path / -s

Path to the source directory containing a COLMAP or Synthetic NeRF data set.

--images / -i

Alternative subdirectory for COLMAP images (images by default).

--eval

Add this flag to use a MipNeRF360-style training/test split for evaluation.

--resolution / -r

Changes the resolution of the loaded images before training. If provided 1, 2, 4 or 8, uses original, 1/2, 1/4 or 1/8 resolution, respectively. For all other values, rescales the width to the given number while maintaining image aspect. 1 by default.

--white_background / -w

Add this flag to use white background instead of black (default), e.g., for evaluation of NeRF Synthetic dataset.

--convert_SHs_python

Flag to make pipeline render with computed SHs from PyTorch instead of ours.

--convert_cov3D_python

Flag to make pipeline render with computed 3D covariance from PyTorch instead of ours.

Command Line Arguments for metrics.py

--model_paths / -m

Space-separated list of model paths for which metrics should be computed.

🙇‍Funding and Acknowledgments

This work was supported by National Natural Science Foundation of China (No. 72192821, 62302296, 62302297, 62272447), Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102), Shanghai Sailing Program (22YF1420300), Young Elite Scientists Sponsorship Program by CAST (2022QNRC001), the Fundamental Research Funds for the Central Universities (project number: YG2023QNA35, YG2023QNB17, YG2024QNA44).

🤝BibTeX

If you find this code helpful for your research, please cite:

@inproceedings{xzwang2024adrgaussian,
  title={AdR-Gaussian: Accelerating Gaussian Splatting with Adaptive Radius},
  author={Wang, Xinzhe and Yi, Ran and Ma, Lizhuang},
  booktitle={ACM SIGGRAPH Asia 2024 Conference Proceedings},
  pages={1--10},
  year={2024}
}