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Instant-ngp in pytorch+cuda trained with pytorch-lightning (high quality with high speed, with only few lines of legible code)
| Date | Stars |
|---|---|
| 2026-07-24 | 1288 |
| 2026-07-25 | 1288 |
| 2026-07-28 | 1288 |
| 2026-07-30 | 1288 |
| 2026-08-06 | 1288 |
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# ngp_pl
### Advertisement: Check out the latest integrated project [nerfstudio](https://github.com/nerfstudio-project/nerfstudio)! There are a lot of recent improvements on nerf related methods, including instant-ngp!
<!-- ### Update 2022 July 29th: GUI prototype is available now (see following video)!
### Update 2022 July 24th: Training on custom data is possible now!
### Update 2022 July 14th: Multi-GPU training is available now! With multiple GPUs, now you can achieve high quality under a minute! -->
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)!
* [Official CUDA implementation](https://github.com/NVlabs/instant-ngp/tree/master)
* [torch-ngp](https://github.com/ashawkey/torch-ngp) another pytorch implementation that I highly referenced.
# :paintbrush: Gallery
https://user-images.githubusercontent.com/11364490/181671484-d5e154c8-6cea-4d52-94b5-1e5dd92955f2.mp4
Other representative videos are in [GALLERY.md](GALLERY.md)
# :computer: Installation
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).
## Hardware
* OS: Ubuntu 20.04
* NVIDIA GPU with Compute Compatibility >= 75 and memory > 6GB (Tested with RTX 2080 Ti), CUDA 11.3 (might work with older version)
* 32GB RAM (in order to load full size images)
## Software
* Clone this repo by `git clone https://github.com/kwea123/ngp_pl`
* Python>=3.8 (installation via [anaconda](https://www.anaconda.com/distribution/) is recommended, use `conda create -n ngp_pl python=3.8` to create a conda environment and activate it by `conda activate ngp_pl`)
* Python libraries
* Install pytorch by `pip install torch==1.11.0 --extra-index-url https://download.pytorch.org/whl/cu113`
* Install `torch-scatter` following their [instruction](https://github.com/rusty1s/pytorch_scatter#installation)
* Install `tinycudann` following their [instruction](https://github.com/NVlabs/tiny-cuda-nn#pytorch-extension) (pytorch extension)
* Install `apex` following their [instruction](https://github.com/NVIDIA/apex#linux)
* Install core requirements by `pip install -r requirements.txt`
* Cuda extension: Upgrade `pip` to >= 22.1 and run `pip install models/csrc/` (please run this each time you `pull` the code)
# :books: Supported Datasets
1. NSVF data
Download preprocessed datasets (`Synthetic_NeRF`, `Synthetic_NSVF`, `BlendedMVS`, `TanksAndTemples`) from [NSVF](https://github.com/facebookresearch/NSVF#dataset). **Do not change the folder names** since there is some hard-coded fix in my dataloader.
2. NeRF++ data
Download data from [here](https://github.com/Kai-46/nerfplusplus#data).
3. Colmap data
For custom data, run `colmap` and get a folder `sparse/0` under which there are `cameras.bin`, `images.bin` and `points3D.bin`. The following data with colmap format are also supported:
* [nerf_llff_data](https://drive.google.com/file/d/16VnMcF1KJYxN9QId6TClMsZRahHNMW5g/view?usp=sharing)
* [mipnerf360 data](http://storage.googleapis.com/gresearch/refraw360/360_v2.zip)
* [HDR-NeRF data](https://drive.google.com/drive/folders/1OTDLLH8ydKX1DcaNpbQ46LlP0dKx6E-I). Additionally, download my colmap pose estimation from [here](https://drive.google.com/file/d/1TXxgf_ZxNB4o67FVD_r0aBUIZVRgZYMX/view?usp=sharing) and extract to the same location.
4. RTMV data
Download data from [here](http://www.cs.umd.edu/~mmeshry/projects/rtmv/). To convert the hdr images into ldr images for training, run `python misc/prepare_rtmv.py <path/to/RTMV>`, it will create `images/` folder under each scene folder, and will use these images to train (and test).
# :key:Excerpt of 8,703 characters
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AI葵 · Luma AI · Taiwan
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:306693e3e6fa6e21, topic:nerf, topic:3d-reconstruction, readme:pose estimation
matched fp:306693e3e6fa6e21, topic:cuda
matched fp:306693e3e6fa6e21, topic:pytorch