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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.
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.
NeRF (Neural Radiance Fields) and NeRF in the Wild using pytorch-lightning
| Date | Stars |
|---|---|
| 2026-07-24 | 2805 |
| 2026-07-25 | 2805 |
| 2026-07-28 | 2805 |
| 2026-07-30 | 2805 |
| 2026-08-09 | 2805 |
| 2026-08-17 | 2804 |
| 2026-08-20 | 2804 |
| 2026-08-21 | 2805 |
| 2026-08-27 | 2804 |
| 2026-09-08 | 2803 |
| 2026-09-15 | 2802 |
| 2026-09-20 | 2801 |
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# nerf_pl
### Update: NVIDIA open-sourced a lightning-fast version of NeRF: [NGP](https://github.com/NVlabs/instant-ngp). I re-implemented in pytorch [here](https://github.com/kwea123/ngp_pl). This version is ~100x faster than this repo with also better quality!
### Update: an improved [NSFF](https://www.cs.cornell.edu/~zl548/NSFF/) implementation to handle dynamic scene is [open](https://github.com/kwea123/nsff_pl)!
### Update: [NeRF-W](https://nerf-w.github.io/) (NeRF in the Wild) implementation is added to [nerfw](https://github.com/kwea123/nerf_pl/tree/nerfw) branch!
### Update: The lastest code (using the latest libraries) will be updated to [dev](https://github.com/kwea123/nerf_pl/tree/dev) branch. The master branch remains to support the colab files. If you don't use colab, it is recommended to switch to dev branch. Only issues of the dev and nerfw branch will be considered currently.
### :gem: [**Project page**](https://kwea123.github.io/nerf_pl/) (live demo!)
Unofficial implementation of [NeRF](https://arxiv.org/pdf/2003.08934.pdf) (Neural Radiance Fields) using pytorch ([pytorch-lightning](https://github.com/PyTorchLightning/pytorch-lightning)). This repo doesn't aim at reproducibility, but aim at providing a simpler and faster training procedure (also simpler code with detailed comments to help to understand the work). Moreover, I try to extend much more opportunities by integrating this algorithm into game engine like Unity.
Official implementation: [nerf](https://github.com/bmild/nerf) .. Reference pytorch implementation: [nerf-pytorch](https://github.com/yenchenlin/nerf-pytorch)
### Recommend to read: A detailed NeRF extension list: [awesome-NeRF](https://github.com/yenchenlin/awesome-NeRF)
## :milky_way: Features
* Multi-gpu training: Training on 8 GPUs finishes within 1 hour for the synthetic dataset!
* [Colab](#mortar_board-colab) notebooks to allow easy usage!
* [Reconstruct](#ribbon-mesh) **colored** mesh!
* [Mixed Reality](https://youtu.be/S5phWFTs2iM) in Unity!
* [REAL TIME volume rendering](https://youtu.be/w9qTbVzCdWk) in Unity!
* [Portable Scenes](#portable-scenes) to let you play with other people's scenes!
### You can find the Unity project including mesh, mixed reality and volume rendering [here](https://github.com/kwea123/nerf_Unity)! See [README_Unity](README_Unity.md) for generating your own data for Unity rendering!
## :beginner: Tutorial
### What can NeRF do?
<img src="https://user-images.githubusercontent.com/11364490/82124460-1ccbbb80-97da-11ea-88ad-25e22868a5c1.png" style="max-width:100%">
### Tutorial videos
<a href="https://www.youtube.com/playlist?list=PLDV2CyUo4q-K02pNEyDr7DYpTQuka3mbV">
<img src="https://user-images.githubusercontent.com/11364490/80913471-d5781080-8d7f-11ea-9f72-9d68402b8271.png">
</a>
# :computer: Installation
## Hardware
* OS: Ubuntu 18.04
* NVIDIA GPU with **CUDA>=10.1** (tested with 1 RTX2080Ti)
## Software
* Clone this repo by `git clone --recursive https://github.com/kwea123/nerf_pl`
* Python>=3.6 (installation via [anaconda](https://www.anaconda.com/distribution/) is recommended, use `conda create -n nerf_pl python=3.6` to create a conda environment and activate it by `conda activate nerf_pl`)
* Python libraries
* Install core requirements by `pip install -r requirements.txt`
* Install `torchsearchsorted` by `cd torchsearchsorted` then `pip install .`
# :key: Training
Please see each subsection for training on different datasets. Available training datasets:
* [Blender](#blender) (Realistic Synthetic 360)
* [LLFF](#llff) (Real Forward-Facing)
* [Your own data](#your-own-data) (Forward-Facing/360 inward-facing)
## Blender
<details>
<summary>Steps</summary>
### Data download
Download `nerf_synthetic.zip` from [here](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1)
### Training model
Run (example)
```
python train.py \
--dataset_name blender \
--root_dir $BLENDER_DIR \
--N_importance 64 Excerpt of 11,593 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:ac9f7afeda9ec65d, topic:nerf, topic:3d-reconstruction, desc:neural radiance