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Instant-ngp (only NeRF) in pytorch+cuda trained with pytorch-lightning (high quality with high speed). This repo aims at providing a concise pytorch interface to facilitate future research, and am grateful if you can share it (and a citation is highly appreciated)!
progress.mp4
lego_trainval_5min_PNSR35.73.mp4
- Official CUDA implementation
- torch-ngp another pytorch implementation that I highly referenced.
This implementation has strict requirements due to dependencies on other libraries, if you encounter installation problem due to hardware/software mismatch, I'm afraid there is no intention to support different platforms (you are welcomed to contribute).
- OS: Ubuntu 20.04
- NVIDIA GPU with Compute Compatibility >= 75 and memory > 8GB (Tested with RTX 2080 Ti), CUDA 11.3 (might work with older version)
-
Clone this repo by
git clone https://github.com/kwea123/ngp_pl
-
Python>=3.8 (installation via anaconda is recommended, use
conda create -n ngp_pl python=3.8
to create a conda environment and activate it byconda activate ngp_pl
) -
Python libraries
- Install pytorch by
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113
- Install
tinycudann
following their instruction (compilation and pytorch extension) - Install
apex
following their instruction - Install core requirements by
pip install -r requirements.txt
- Install pytorch by
-
Cuda extension: Upgrade
pip
to >= 22.1 and runpip install models/csrc/
Download preprocessed datasets from NSVF.
Quickstart: python train.py --root_dir <path/to/lego> --exp_name Lego
It will train the lego scene for 20k steps (each step with 8192 rays), and perform one testing at the end. The training process should finish within about 5 minutes (saving testing image is slow, add --no_save_test
to disable). Testing PSNR will be shown at the end.
More options can be found in opt.py.
Use test.ipynb
to generate images. Lego pretrained model is available here
I compared the quality (average testing PSNR on Synthetic-NeRF
) and the inference speed (on Lego
scene) v.s. the concurrent work torch-ngp (default settings) and the paper, all trained for about 5 minutes:
Method | avg PSNR | FPS |
---|---|---|
torch-ngp | 31.46 | 18.2 |
mine | 32.38 | 36.2 |
instant-ngp paper | 33.18 | 60 |
As for quality, mine is slightly better than torch-ngp, but the result might fluctuate across different runs.
As for speed, mine is faster than torch-ngp, but is still only half fast as instant-ngp. Speed is dependent on the scene (if most of the scene is empty, speed will be faster).
More details are in the following section.
To run benchmarks, use the scripts under benchmarking
.
Followings are my results:
Synthetic-NeRF
Mic | Ficus | Chair | Hotdog | Materials | Drums | Ship | Lego | AVG | |
---|---|---|---|---|---|---|---|---|---|
PSNR | 35.23 | 33.64 | 34.78 | 36.76 | 28.77 | 25.61 | 29.57 | 34.69 | 32.38 |
FPS | 40.81 | 34.02 | 49.80 | 25.06 | 20.08 | 37.77 | 15.77 | 36.20 | 32.44 |
Synthetic-NSVF
Wineholder | Steamtrain | Toad | Robot | Bike | Palace | Spaceship | Lifestyle | AVG | |
---|---|---|---|---|---|---|---|---|---|
PSNR | 31.06 | 35.65 | 34.49 | 36.23 | 36.99 | 36.36 | 35.48 | 33.96 | 35.03 |
FPS | 47.07 | 75.17 | 50.42 | 64.87 | 66.88 | 28.62 | 35.55 | 22.84 | 48.93 |
Tanks and Temples
Ignatius | Truck | Barn | Caterpillar | Family | AVG | |
---|---|---|---|---|---|---|
PSNR | 28.90 | 28.21 | 28.92 | 26.30 | 33.77 | 29.22 |
*FPS | 10.04 | 7.99 | 16.14 | 10.91 | 6.16 | 10.25 |
*Evaluated on test-traj
BlendedMVS
*Jade | *Fountain | Character | Statues | AVG | |
---|---|---|---|---|---|
PSNR | 25.69 | 26.91 | 30.16 | 26.93 | 27.42 |
**FPS | 26.02 | 21.24 | 35.99 | 19.22 | 25.61 |
*I manually switch the background from black to white, so the number isn't directly comparable to that in the papers.
**Evaluated on test-traj
- support custom dataset