torch-ngp

This repository contains:

• A pytorch implementation of the SDF and NeRF part (grid encoder, density grid ray sampler) in instant-ngp, as described in Instant Neural Graphics Primitives with a Multiresolution Hash Encoding.
• A pytorch implementation of TensoRF, as described in TensoRF: Tensorial Radiance Fields, adapted to instant-ngp's NeRF framework.
• A pytorch implementation of CCNeRF, as described in Compressible-composable NeRF via Rank-residual Decomposition.
• [New!] An implementation of D-NeRF adapted to instant-ngp's framework, as described in D-NeRF: Neural Radiance Fields for Dynamic Scenes.
• Some experimental features in the NeRF framework (e.g., text-guided NeRF editig similar to CLIP-NeRF).
• A GUI for training/visualizing NeRF!

Gallery | Update Logs

nerf.mp4

dnerf.mp4

Install

git clone --recursive https://github.com/ashawkey/torch-ngp.git
cd torch-ngp

Install with pip

pip install -r requirements.txt

# (optional) install the tcnn backbone
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch

Install with conda

conda env create -f environment.yml
conda activate torch-ngp

Build extension (optional)

By default, we use load to build the extension at runtime. However, this may be inconvenient sometimes. Therefore, we also provide the setup.py to build each extension:

# install all extension modules
bash scripts/install_ext.sh

# if you want to install manually, here is an example:
cd raymarching
python setup.py build_ext --inplace # build ext only, do not install (only can be used in the parent directory)
pip install . # install to python path (you still need the raymarching/ folder, since this only install the built extension.)

Tested environments

• Ubuntu 20 with torch 1.10 & CUDA 11.3 on a TITAN RTX.
• Ubuntu 16 with torch 1.8 & CUDA 10.1 on a V100.
• Windows 10 with torch 1.11 & CUDA 11.3 on a RTX 3070.

Currently, --ff only supports GPUs with CUDA architecture >= 70. For GPUs with lower architecture, --tcnn can still be used, but the speed will be slower compared to more recent GPUs.

Usage

We use the same data format as instant-ngp, e.g., armadillo and fox. Please download and put them under ./data.

We also support self-captured dataset and converting other formats (e.g., LLFF, Tanks&Temples, Mip-NeRF 360) to the nerf-compatible format, with details in the following code block.

Supported datasets

• nerf_synthetic

• Tanks&Temples: [conversion script]

• LLFF: [conversion script]

• Mip-NeRF 360: [conversion script]

• (dynamic) D-NeRF

• (dynamic) Hyper-NeRF: [conversion script]

First time running will take some time to compile the CUDA extensions.

### Instant-ngp NeRF
# train with different backbones (with slower pytorch ray marching)
# for the colmap dataset, the default dataset setting `--bound 2 --scale 0.33` is used.
python main_nerf.py data/fox --workspace trial_nerf # fp32 mode
python main_nerf.py data/fox --workspace trial_nerf --fp16 # fp16 mode (pytorch amp)
python main_nerf.py data/fox --workspace trial_nerf --fp16 --ff # fp16 mode + FFMLP (this repo's implementation)
python main_nerf.py data/fox --workspace trial_nerf --fp16 --tcnn # fp16 mode + official tinycudann's encoder & MLP

# use CUDA to accelerate ray marching (much more faster!)
python main_nerf.py data/fox --workspace trial_nerf --fp16 --cuda_ray # fp16 mode + cuda raymarching

# preload data into GPU, accelerate training but use more GPU memory.
python main_nerf.py data/fox --workspace trial_nerf --fp16 --preload

# one for all: -O means --fp16 --cuda_ray --preload, which usually gives the best results balanced on speed & performance.
python main_nerf.py data/fox --workspace trial_nerf -O

# test mode
python main_nerf.py data/fox --workspace trial_nerf -O --test

# construct an error_map for each image, and sample rays based on the training error (slow down training but get better performance with the same number of training steps)
python main_nerf.py data/fox --workspace trial_nerf -O --error_map

# use a background model (e.g., a sphere with radius = 32), can supress noises for real-world 360 dataset
python main_nerf.py data/firekeeper --workspace trial_nerf -O --bg_radius 32

# start a GUI for NeRF training & visualization
# always use with `--fp16 --cuda_ray` for an acceptable framerate!
python main_nerf.py data/fox --workspace trial_nerf -O --gui

# test mode for GUI
python main_nerf.py data/fox --workspace trial_nerf -O --gui --test

# for the blender dataset, you should add `--bound 1.0 --scale 0.8 --dt_gamma 0`
# --bound means the scene is assumed to be inside box[-bound, bound]
# --scale adjusts the camera locaction to make sure it falls inside the above bounding box. 
# --dt_gamma controls the adaptive ray marching speed, set to 0 turns it off.
python main_nerf.py data/nerf_synthetic/lego --workspace trial_nerf -O --bound 1.0 --scale 0.8 --dt_gamma 0
python main_nerf.py data/nerf_synthetic/lego --workspace trial_nerf -O --bound 1.0 --scale 0.8 --dt_gamma 0 --gui

# for the LLFF dataset, you should first convert it to nerf-compatible format:
python scripts/llff2nerf.py data/nerf_llff_data/fern # by default it use full-resolution images, and write `transforms.json` to the folder
python scripts/llff2nerf.py data/nerf_llff_data/fern --images images_4 --downscale 4 # if you prefer to use the low-resolution images
# then you can train as a colmap dataset (you'll need to tune the scale & bound if necessary):
python main_nerf.py data/nerf_llff_data/fern --workspace trial_nerf -O
python main_nerf.py data/nerf_llff_data/fern --workspace trial_nerf -O --gui

# for the Tanks&Temples dataset, you should first convert it to nerf-compatible format:
python scripts/tanks2nerf.py data/TanksAndTemple/Family # write `trainsforms_{split}.json` for [train, val, test]
# then you can train as a blender dataset (you'll need to tune the scale & bound if necessary)
python main_nerf.py data/TanksAndTemple/Family --workspace trial_nerf_family -O --bound 1.0 --scale 0.33 --dt_gamma 0
python main_nerf.py data/TanksAndTemple/Family --workspace trial_nerf_family -O --bound 1.0 --scale 0.33 --dt_gamma 0 --gui

# for custom dataset, you should:
# 1. take a video / many photos from different views 
# 2. put the video under a path like ./data/custom/video.mp4 or the images under ./data/custom/images/*.jpg.
# 3. call the preprocess code: (should install ffmpeg and colmap first! refer to the file for more options)
python scripts/colmap2nerf.py --video ./data/custom/video.mp4 --run_colmap # if use video
python scripts/colmap2nerf.py --images ./data/custom/images/ --run_colmap # if use images
python scripts/colmap2nerf.py --video ./data/custom/video.mp4 --run_colmap --dynamic # if the scene is dynamic (for D-NeRF settings), add the time for each frame.
# 4. it should create the transform.json, and you can train with: (you'll need to try with different scale & bound & dt_gamma to make the object correctly located in the bounding box and render fluently.)
python main_nerf.py data/custom --workspace trial_nerf_custom -O --gui --scale 2.0 --bound 1.0 --dt_gamma 0.02

### Instant-ngp SDF
python main_sdf.py data/armadillo.obj --workspace trial_sdf
python main_sdf.py data/armadillo.obj --workspace trial_sdf --fp16
python main_sdf.py data/armadillo.obj --workspace trial_sdf --fp16 --ff
python main_sdf.py data/armadillo.obj --workspace trial_sdf --fp16 --tcnn

python main_sdf.py data/armadillo.obj --workspace trial_sdf --fp16 --test

### TensoRF
# almost the same as Instant-ngp NeRF, just replace the main script.
python main_tensoRF.py data/fox --workspace trial_tensoRF -O
python main_tensoRF.py data/nerf_synthetic/lego --workspace trial_tensoRF -O --bound 1.0 --scale 0.8 --dt_gamma 0 

### CCNeRF
# training on single objects, turn on --error_map for better quality.
python main_CCNeRF.py data/nerf_synthetic/chair --workspace trial_cc_chair -O --bound 1.0 --scale 0.67 --dt_gamma 0 --error_map
python main_CCNeRF.py data/nerf_synthetic/ficus --workspace trial_cc_ficus -O --bound 1.0 --scale 0.67 --dt_gamma 0 --error_map
python main_CCNeRF.py data/nerf_synthetic/hotdog --workspace trial_cc_hotdog -O --bound 1.0 --scale 0.67 --dt_gamma 0 --error_map
# compose, use a larger bound and more samples per ray for better quality.
python main_CCNeRF.py data/nerf_synthetic/hotdog --workspace trial_cc_hotdog -O --bound 2.0 --scale 0.67 --dt_gamma 0 --max_steps 2048 --test --compose
# compose + gui, only about 1 FPS without dynamic resolution... just for quick verification of composition results.
python main_CCNeRF.py data/nerf_synthetic/hotdog --workspace trial_cc_hotdog -O --bound 2.0 --scale 0.67 --dt_gamma 0 --test --compose --gui

### D-NeRF
# almost the same as Instant-ngp NeRF, just replace the main script.
python main_dnerf.py data/dnerf/jumpingjacks --workspace trial_dnerf_jumpingjacks -O --bound 1.0 --scale 0.8 --dt_gamma 0
python main_dnerf.py data/dnerf/jumpingjacks --workspace trial_dnerf_jumpingjacks -O --bound 1.0 --scale 0.8 --dt_gamma 0 --gui
# for the hypernerf dataset, first convert it into nerf-compatible format:
python scripts/hyper2nerf.py data/split-cookie --downscale 2 # will generate transforms*.json
python main_dnerf.py data/split-cookie/ --workspace trial_dnerf_cookies -O --bound 1 --scale 0.3 --dt_gamma 0

check the scripts directory for more provided examples.

Performance Reference

Tested with the default settings on the Lego dataset. Here the speed refers to the iterations per second on a V100.

Model	Split	PSNR	Train Speed	Test Speed
instant-ngp (paper)	trainval?	36.39	-	-
instant-ngp (-O)	train (30K steps)	34.15	97	7.8
instant-ngp (-O --error_map)	train (30K steps)	34.88	50	7.8
instant-ngp (-O)	trainval (40k steps)	35.22	97	7.8
instant-ngp (-O --error_map)	trainval (40k steps)	36.00	50	7.8
TensoRF (paper)	train (30K steps)	36.46	-	-
TensoRF (-O)	train (30K steps)	35.05	51	2.8
TensoRF (-O --error_map)	train (30K steps)	35.84	14	2.8

Tips

Q: How to choose the network backbone?

A: The -O flag which uses pytorch's native mixed precision is suitable for most cases. I don't find very significant improvement for --tcnn and --ff, and they require extra building. Also, some new features may only be available for the default -O mode.

Q: CUDA Out Of Memory for my dataset.

A: You could try to turn off --preload which loads all images in to GPU for acceleration (if use -O, change it to --fp16 --cuda_ray). Another solution is to manually set downscale in NeRFDataset to lower the image resolution.

Q: How to adjust bound and scale?

A: You could start with a large bound (e.g., 16) or a small scale (e.g., 0.3) to make sure the object falls into the bounding box. The GUI mode can be used to interactively shrink the bound to find the suitable value. Uncommenting this line will visualize the camera poses, and some good examples can be found in this issue.

Q: Noisy novel views for realistic datasets.

A: You could try setting bg_radius to a large value, e.g., 32. It trains an extra environment map to model the background in realistic photos. A larger bound will also help. An example for bg_radius in the firekeeper dataset:

Difference from the original implementation

• Instead of assuming the scene is bounded in the unit box [0, 1] and centered at (0.5, 0.5, 0.5), this repo assumes the scene is bounded in box [-bound, bound], and centered at (0, 0, 0). Therefore, the functionality of aabb_scale is replaced by bound here.
• For the hashgrid encoder, this repo only implements the linear interpolation mode.
• For TensoRF, we don't implement regularizations other than L1, and use trunc_exp as the density activation instead of softplus. The alpha mask pruning is replaced by the density grid sampler from instant-ngp, which shares the same logic for acceleration.

Citation

If you find this work useful, a citation will be appreciated via:

@misc{torch-ngp,
    Author = {Jiaxiang Tang},
    Year = {2022},
    Note = {https://github.com/ashawkey/torch-ngp},
    Title = {Torch-ngp: a PyTorch implementation of instant-ngp}
}

@article{tang2022compressible,
    title = {Compressible-composable NeRF via Rank-residual Decomposition},
    author = {Tang, Jiaxiang and Chen, Xiaokang and Wang, Jingbo and Zeng, Gang},
    journal = {arXiv preprint arXiv:2205.14870},
    year = {2022}
}

Acknowledgement

• Credits to Thomas Müller for the amazing tiny-cuda-nn and instant-ngp:

  @misc{tiny-cuda-nn,
      Author = {Thomas M\"uller},
      Year = {2021},
      Note = {https://github.com/nvlabs/tiny-cuda-nn},
      Title = {Tiny {CUDA} Neural Network Framework}
  }
  
  @article{mueller2022instant,
      title = {Instant Neural Graphics Primitives with a Multiresolution Hash Encoding},
      author = {Thomas M\"uller and Alex Evans and Christoph Schied and Alexander Keller},
      journal = {arXiv:2201.05989},
      year = {2022},
      month = jan
  }
  

• The framework of NeRF is adapted from nerf_pl:

  @misc{queianchen_nerf,
      author = {Quei-An, Chen},
      title = {Nerf_pl: a pytorch-lightning implementation of NeRF},
      url = {https://github.com/kwea123/nerf_pl/},
      year = {2020},
  }
  

• The official TensoRF implementation:

  @article{TensoRF,
    title={TensoRF: Tensorial Radiance Fields},
    author={Chen, Anpei and Xu, Zexiang and Geiger, Andreas and Yu, Jingyi and Su, Hao},
    journal={arXiv preprint arXiv:2203.09517},
    year={2022}
  }
  

• The NeRF GUI is developed with DearPyGui.