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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A pytorch CUDA extension implementation of instant-ngp (sdf and nerf), with a GUI.
| Date | Stars |
|---|---|
| 2026-07-24 | 2213 |
| 2026-07-25 | 2213 |
| 2026-07-28 | 2213 |
| 2026-07-30 | 2213 |
| 2026-08-06 | 2213 |
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# torch-ngp This repository contains: * A pytorch implementation of the SDF and NeRF part (grid encoder, density grid ray sampler) in [instant-ngp](https://github.com/NVlabs/instant-ngp), as described in [_Instant Neural Graphics Primitives with a Multiresolution Hash Encoding_](https://nvlabs.github.io/instant-ngp/assets/mueller2022instant.pdf). * A pytorch implementation of [TensoRF](https://github.com/apchenstu/TensoRF), as described in [_TensoRF: Tensorial Radiance Fields_](https://arxiv.org/abs/2203.09517), adapted to instant-ngp's NeRF framework. * A pytorch implementation of [CCNeRF](https://github.com/ashawkey/CCNeRF), as described in [_Compressible-composable NeRF via Rank-residual Decomposition_](https://arxiv.org/abs/2205.14870). * [New!] An implementation of [D-NeRF](https://github.com/albertpumarola/D-NeRF) adapted to instant-ngp's framework, as described in [_D-NeRF: Neural Radiance Fields for Dynamic Scenes_](https://openaccess.thecvf.com/content/CVPR2021/papers/Pumarola_D-NeRF_Neural_Radiance_Fields_for_Dynamic_Scenes_CVPR_2021_paper.pdf). * Some experimental features in the NeRF framework (e.g., text-guided NeRF editig similar to [CLIP-NeRF](https://arxiv.org/abs/2112.05139)). * A GUI for training/visualizing NeRF! **News**: A clean and improved version focusing on static NeRF reconstruction of realistic scenes has been separated into [nerf_template](https://github.com/ashawkey/nerf_template), as this repository has been hard to maintain. ### [Gallery](assets/gallery.md) | [Update Logs](assets/update_logs.md) Instant-ngp interactive training/rendering on lego: https://user-images.githubusercontent.com/25863658/176174011-e7b7c4ab-9b6f-4f65-9952-7eceafe609b7.mp4 Also the first interactive deformable-nerf implementation: https://user-images.githubusercontent.com/25863658/175821784-63ba79f6-29be-47b5-b3fc-dab5282fce7a.mp4 ### Other related projects * [ngp_pl](https://github.com/kwea123/ngp_pl): PyTorch+CUDA trained with pytorch-lightning. * [JNeRF](https://github.com/Jittor/JNeRF): An NeRF benchmark based on Jittor. * [HashNeRF-pytorch](https://github.com/yashbhalgat/HashNeRF-pytorch): A pure PyTorch implementation. * [dreamfields-torch](https://github.com/ashawkey/dreamfields-torch): PyTorch+CUDA implementation of [_Zero-Shot Text-Guided Object Generation with Dream Fields_](https://arxiv.org/abs/2112.01455) based on this repository. # Install ```bash git clone --recursive https://github.com/ashawkey/torch-ngp.git cd torch-ngp ``` ### Install with pip ```bash 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 ```bash conda env create -f environment.yml conda activate torch-ngp ``` ### Build extension (optional) By default, we use [`load`](https://pytorch.org/docs/stable/cpp_extension.html#torch.utils.cpp_extension.load) to build the extension at runtime. However, this may be inconvenient sometimes. Therefore, we also provide the `setup.py` to build each extension: ```bash # 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](https://github.com/NVlabs/instant-ngp/blob/master/data/sdf/armadillo.obj) and [fox](http
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kiui · NVIDIA · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c74a8d858960ea8f, topic:nerf, readme:neural radiance
matched fp:c74a8d858960ea8f, topic:pytorch