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https://github.com/WangFeng18/3d-gaussian-splatting
Implementation for 3d gaussian splatting
https://github.com/WangFeng18/3d-gaussian-splatting
gaussian-splatting nerf pytorch real-time-rendering
Last synced: 3 months ago
JSON representation
Implementation for 3d gaussian splatting
- Host: GitHub
- URL: https://github.com/WangFeng18/3d-gaussian-splatting
- Owner: WangFeng18
- License: mit
- Created: 2023-06-14T14:44:26.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-17T03:15:21.000Z (about 1 year ago)
- Last Synced: 2024-08-04T05:01:35.017Z (6 months ago)
- Topics: gaussian-splatting, nerf, pytorch, real-time-rendering
- Language: Python
- Homepage:
- Size: 12.6 MB
- Stars: 313
- Watchers: 14
- Forks: 16
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-3D-gaussian-splatting - 3D Gaussian Splatting
README
# 3d-Gaussian-Splatting
An unofficial Implementation of 3D Gaussian Splatting for Real-Time Radiance Field Rendering [SIGGRAPH 2023].
We implement the 3d gaussian splatting methods through PyTorch with CUDA extensions, including the global culling, tile-based culling and rendering forward/backward codes.
Work in progress.
#### Update
- 6/26/2023 Fix bugs of SSIM criterion, PSNR is improved from 24.28 to 24.85 (Garden Scene)
- 6/26/2023 Accelerate **Training** Speed from avg 4 it/s to 13 it/s, by (1) replacing part of atomicAdd by warp reduction primitive (2) fixing bugs for SSIM functions. The training costs 9 minutes for 7k iterations on Garden scene.
| Scene | PSNR from paper | PSNR from this repo | Rendering Speed (official) | Rendering Speed (Ours) |
| --- | --- | --- | --- | --- |
| Garden | 25.82(5k) | 24.91 (7k) | 160 FPS (avg MIPNeRF360) | 60 FPS |
| Garden | 25.82(5k) | 25.70 (7k) | 160 FPS (avg MIPNeRF360) | 25 FPS |
https://github.com/WangFeng18/3d-gaussian-splatting/assets/43294876/79703b5d-50ae-404b-96c9-c73690646f34
QuickStart
#### Install CUDA Extensions
```
# compile CUDA extension
pip install -e ./
```
#### Data Preparation
Put the colmap output in this folder, e.g., colmap_garden/sparse/0/, as well as the images.
### Traning
```
python train.py --exp garden --grad_thresh 0.000004 --debug 1 --ssim_weight 0.1 --lr 0.002 --use_sh_coeff 0 --grad_accum_method mean --grad_accum_iters 300 --split_thresh 0.08 # PSNR 24.75 SSIM 71.95 FPS 70 N_Gaussians 376467
python train.py --exp garden --grad_thresh 0.000004 --debug 1 --ssim_weight 0.1 --lr 0.002 --use_sh_coeff 0 --grad_accum_method mean --grad_accum_iters 300 # PSNR 25.03 SSIM 0.7541 FPS 40 N_GAUSSIANS 933918
python train.py --exp garden --grad_thresh 0.000002 --debug 1 --ssim_weight 0.1 --lr 0.002 --use_sh_coeff 0 --grad_accum_method mean --grad_accum_iters 300 --split_thresh 0.08 # PSNR 24.91 SSIM 73.18 FPS 64 N_GAUSSIANS 506627 GOOD
python train.py --exp garden2 --grad_thresh 0.000004 --debug 1 --ssim_weight 0.2 --lr 0.002 --use_sh_coeff 0 --grad_accum_method mean --grad_accum_iters 300 --adaptive_control_end_iter 3000 --opa_init_value 0.05 --lr_factor_for_opa 20 # PSNR 25.55 SSIM 79.83 N_GAUSSIANS 2418528 FPS 24.68
CUDA_VISIBLE_DEVICES=3 python train.py --exp garden2 --grad_thresh 0.000004 --debug 1 --ssim_weight 0.2 --lr 0.002 --use_sh_coeff 0 --grad_accum_method mean --grad_accum_iters 300 --adaptive_control_end_iter 3000 --opa_init_value 0.05 --lr_factor_for_opa 20 # PSNR 25.5586 SSIM 80.10 FPS 25.30 N_GAUSSIANS 2401413
python train.py --exp garden2 --grad_thresh 0.000004 --debug 1 --ssim_weight 0.2 --lr 0.002 --use_sh_coeff 0 --grad_accum_method mean --grad_accum_iters 300 --adaptive_control_end_iter 3000 --opa_init_value 0.05 --lr_factor_for_opa 20 --lr_factor_for_scale 0.2 --lr_factor_for_quat 10 --split_thresh 0.05 #PSNR 24.896 SSIM 76.55 FPS 65 N_GAUSSIANS 765932
python train.py --exp garden2 --grad_thresh 0.000004 --debug 1 --ssim_weight 0.2 --lr 0.002 --use_sh_coeff 0 --grad_accum_method mean --grad_accum_iters 300 --adaptive_control_end_iter 3000 --opa_init_value 0.05 --lr_factor_for_opa 20 --lr_factor_for_quat 10 # PSNR 25.6906 SSIM 80.66 FPS 24.68
python train.py --exp garden2 --grad_thresh 0.000004 --debug 1 --ssim_weight 0.2 --lr 0.002 --use_sh_coeff 0 --grad_accum_method mean --grad_accum_iters 300 --adaptive_control_end_iter 3000 --opa_init_value 0.05 --lr_factor_for_opa 20 --lr_factor_for_scale 0.5 --lr_factor_for_quat 10 --split_thresh 0.05 # PSNR 25.3769 SSIM 0.7902 FPS 41.3186
CUDA_VISIBLE_DEVICES=3 python train.py --exp garden2 --grad_thresh 0.000004 --debug 1 --ssim_weight 0.2 --lr 0.002 --use_sh_coeff 0 --grad_accum_method mean --grad_accum_iters 300 --adaptive_control_end_iter 3000 --opa_init_value 0.05 --lr_factor_for_opa 20 --lr_factor_for_quat 20 # PSNR 25.7021 SSIM 0.8052 FPS 25.3567
```
### Rendering With a GUI
```
python train.py --ckpt ckpt.pth --gui 1 --test 1
```
The GUI is based on [Viser](https://github.com/nerfstudio-project/viser) and written by [ZiLong Chen](https://github.com/heheyas).
The transforms folder are from [Viser](https://github.com/nerfstudio-project/viser)
### Link
Another good implementation for 3D gaussian splatting, by [Zilong Chen](https://github.com/heheyas/gaussian_splatting_3d)