torch-ngp

A pytorch implementation of instant-ngp, as described in Instant Neural Graphics Primitives with a Multiresolution Hash Encoding.

A GUI for training/visualizing NeRF is also available!

https://user-images.githubusercontent.com/25863658/155265815-c608254f-2f00-4664-a39d-e00eae51ca59.mp4

Install

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

Install requirements 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 requirements with conda

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

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.

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

### HashNeRF
# train with different backbones (with slower pytorch ray marching)
# for the colmap dataset, the default dataset setting `--mode colmap --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

# test mode
python main_nerf.py data/fox --workspace trial_nerf --fp16 --ff --test

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

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

# construct an error_map for each image, and sample rays based on previous training error (experimental)
python main_nerf.py data/fox --workspace trial_nerf --fp16 --ff --preload --error_map

# 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

# start a GUI for NeRF training & visualization
# always use with `--fp16 --ff/tcnn --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 `--mode blender --bound 1.0 --scale 0.8 --dt_gamma 0`
# --mode specifies dataset type ('blender' or 'colmap')
# --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 --mode blender --bound 1.0 --scale 0.8 --dt_gamma 0 
python main_nerf.py data/nerf_synthetic/lego --workspace trial_nerf -O --mode blender --bound 1.0 --scale 0.8 --dt_gamma 0 --gui

# for the LLFF dataset, you should first convert it to nerf-compatible format:
python llff2nerf.py data/nerf_llff_data/fern # by default it use full-resolution images, and write `transforms.json` to the folder
python 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 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 colmap2nerf.py --video ./data/custom/video.mp4 --run_colmap # if use video
python colmap2nerf.py --images ./data/custom/images/ --run_colmap # if use images
# 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

### 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 --ff --test

### TensoRF
# almost the same as HashNeRF, 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 --mode blender --bound 1.0 --scale 0.8 --dt_gamma 0 

### DyNerf
####  Dataset: /home/xuhangkun/Metaverse/recon_3d/dataset/coffee_martini
#### Train:
python main_dynerf.py /path/to/dataset --workspace /path/to/workspace -O --scale 0.1
# some parameters
# error_map : isg , ist (refer to DyNerf paper : Ray Importance Sample), error_map

#### Test
python main_dynerf.py /path/to/dataset --workspace /path/to/workspace -O --scale 0.1 --test

check the scripts directory for more provided examples.

Performance Reference

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 the blender dataest, the default mode in instant-ngp is to load all data (train/val/test) for training. Instead, we only use the specified split to train in CMD mode for easy evaluation. However, for GUI mode, we follow instant-ngp and use all data to train (check type='all' for NeRFDataset).
• For TensoRF, we don't implement AABB shrinking and regularizations other than L1.

Progress

As the official pytorch extension tinycudann has been released, the following implementations can be used as modular alternatives. The performance and speed of these modules are guaranteed to be on-par, and we support using tinycudann as the backbone by the --tcnn flag.

• Fully-fused MLP
  • [x] basic pytorch binding of the original implementation
• HashGrid Encoder
  • [x] basic pytorch CUDA extension
  • [x] fp16 support
• Experiments
  • SDF
    • [x] baseline
    • [ ] better SDF calculation (especially for non-watertight meshes)
  • NeRF
    • [x] baseline
    • [x] ray marching in CUDA.
• NeRF GUI
  • [x] supports training.
• Misc.
  • [x] improve rendering quality of cuda raymarching!
  • [ ] improve speed (e.g., avoid the cat in NeRF forward)
  • [ ] support visualize/supervise normals (add rendering mode option).
  • [x] support blender dataset format.

Update Log

• 4.13: add LLFF dataset support.
• 4.13: also implmented tiled grid encoder according to this issue.
• 4.12: optimized dataloader, add error_map sampling (experimental, will slow down training since will only sample hard rays...)
• 4.10: add Windows support.
• 4.9: use 6D AABB instead of a single bound for more flexible rendering. More options in GUI to control the AABB and dt_gamma for adaptive ray marching.
• 4.9: implemented multi-res density grid (cascade) and adaptive ray marching. Now the fox renders much faster!
• 4.6: fixed TensorCP hyper-parameters.
• 4.3: add mark_untrained_grid to prevent training on out-of-camera regions. Add custom dataset instructions.
• 3.31: better compatibility for lower pytorch versions.
• 3.29: fix training speed for the fox dataset (balanced speed with performance...).
• 3.27: major update. basically improve performance, and support tensoRF model.
• 3.22: reverted from pre-generating rays as it takes too much CPU memory, still the PSNR for Lego can reach ~33 now.
• 3.14: fixed the precision related issue for fp16 mode, and it renders much better quality. Added PSNR metric for NeRF.
• 3.14: linearly scale desired_resolution with bound according to https://github.com/ashawkey/torch-ngp/issues/23.
• 3.11: raymarching now supports supervising weights_sum (pixel alpha, or mask) directly, and bg_color is separated from CUDA to make it more flexible. Add an option to preload data into GPU.
• 3.9: add fov for gui.
• 3.1: add type='all' for blender dataset (load train + val + test data), which is the default behavior of instant-ngp.
• 2.28: density_grid now stores density on the voxel center (with randomness), instead of on the grid. This should improve the rendering quality, such as the black strips in the lego scene.
• 2.23: better support for the blender dataset.
• 2.22: add GUI for NeRF training.
• 2.21: add GUI for NeRF visualizing.
• 2.20: cuda raymarching is finally stable now!
• 2.15: add the official tinycudann as an alternative backend.
• 2.10: add cuda_ray, can train/infer faster, but performance is worse currently.
• 2.6: add support for RGBA image.
• 1.30: fixed atomicAdd() to use __half2 in HashGrid Encoder's backward, now the training speed with fp16 is as expected!
• 1.29: finished an experimental binding of fully-fused MLP. replace SHEncoder with a CUDA implementation.
• 1.26: add fp16 support for HashGrid Encoder (requires CUDA >= 10 and GPU ARCH >= 70 for now...).

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:

  @misc{TensoRF,
      title={TensoRF: Tensorial Radiance Fields},
      author={Anpei Chen and Zexiang Xu and Andreas Geiger and and Jingyi Yu and Hao Su},
      year={2022},
      eprint={2203.09517},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
  }

• The NeRF GUI is developed with DearPyGui.