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nerf-factory

An awesome PyTorch NeRF library

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创建于 2022-09-20 · 更新于 2026-10-13 · 今日第 10878 名
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kakaobrain/nerf-factory 排名第 10,878,有 1,272 个星标。

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kakaobrain/nerf-factory 排名第 10,878,此处有 8 个同类条目;google-research/deduplicate-text-datasets 排名第 10,877。

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关键参数与规格
关键参数与规格
参数值
语言Python
类别Model Development
子类别Architecture
关键词3D
星标数1,272
分叉数103
贡献者4
创建日期2022-09-20
更新日期2026-10-13
1 天星标数0
7 天星标数1
排名10,878
上次排名10,848
排名变化-30
7 天星标排名9,988
上次 7 天星标排名10,035
7 天星标排名变化47
总星标排名9,238
上次总星标排名9,227
总星标排名变化-11
国家South Korea
README 摘录

Deprecated Repository This repository is no longer maintained. Please note that all further development and updates have been moved to a new repository. You can find the new repository **here**. Thank you for your understanding and continued support. NeRF-Factory: An awesome PyTorch NeRF collection Project Page | Checkpoints Attention all NeRF researchers! We are here with a PyTorch-reimplemented large-scale NeRF library. Our library is easily extensible and usable. This contains PyTorch-implementation of 7 popular NeRF models. NeRF

: [[Project Page]]( [[Paper]]( [[Code]]( NeRF++: [[Paper]]( [[Code]]( DVGO: [[Project Page]]( [[Paper-v1]]( [[Paper-v2]]( [[Code]]( Plenoxels: [[Project Page]]( [[Paper]]( [[Code]]( Mip-NeRF: [[Project Page]]( [[Paper]]( [[Code]]( Mip-NeRF360: [[Project Page]]( [[Paper]]( [[Code]]( Ref-NeRF: [[Project Page]]( [[Paper]]( [[Code]]( and also 7 popular NeRF datasets. NeRF Blender: link NeRF LLFF: link Tanks and Temples: link LF: link NeRF-360: link NeRF-360-v2: link Shiny Blender: link You only need to do for running the code is: We also provide convenient

visualizers for NeRF researchers. Contributor This project is created and maintained by Yoonwoo Jeong, Seungjoo Shin, and Kibaek Park. Requirements Command Preparing Dataset We provide an automatic download script for all datasets. Training Code A script for running the training code. Evaluation Code A script for running the evaluation code only. Custom How to add the custom dataset and the custom model in NeRF-Factory? Custom Dataset Add files of the custom dataset on . Add option of selecting the custom

dataset on the function of . Custom Model Implement a custom model code on . Implement a custom model's helper code on . [Optional] If you need more code files for the custom model, you can add them in . - Add option of selecting the custom model on the function of . License Copyright (c) 2022 POSTECH, KAIST, and Kakao Brain Corp. Licensed under the Apache License, Version 2.

常见问题

记录了多少星标和分叉?

kakaobrain/nerf-factory 有 1,272 个星标和 103 个分叉。

仓库排名是多少?

kakaobrain/nerf-factory 排名第 10,878。

记录的语言和类别是什么?

kakaobrain/nerf-factory 有 1,272 个星标,语言为 Python,类别为 Model Development。

哪个仓库排名最接近?

kakaobrain/nerf-factory 与 google-research/deduplicate-text-datasets 比较,后者排名第 10,877,有 1,273 个星标。

README

Deprecated Repository

This repository is no longer maintained. Please note that all further development and updates have been moved to a new repository.

You can find the new repository here.

Thank you for your understanding and continued support.

NeRF-Factory: An awesome PyTorch NeRF collection

Project Page | Checkpoints

Attention all NeRF researchers! We are here with a PyTorch-reimplemented large-scale NeRF library. Our library is easily extensible and usable.

This contains PyTorch-implementation of 7 popular NeRF models.

and also 7 popular NeRF datasets.

You only need to do for running the code is:

python3 -m run --ginc configs/[model]/[data].gin
# ex) python3 -m run --ginc configs/nerf/blender.gin

We also provide convenient visualizers for NeRF researchers.

Contributor

This project is created and maintained by Yoonwoo Jeong, Seungjoo Shin, and Kibaek Park.

Requirements

conda create -n nerf_factory -c anaconda python=3.8
conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
pip3 install -r requirements.txt

## Optional(Plenoxel)
pip3 install .

## Or you could directly build from nerf_factory.yml
conda env create --file nerf_factory.yml

Command

python3 -m run --ginc configs/[model]/[data].gin
# ex) python3 -m run --ginc configs/nerf/blender.gin

Preparing Dataset

We provide an automatic download script for all datasets.

# NeRF-blender dataset
bash scripts/download_data.sh nerf_synthetic
# NeRF-LLFF(NeRF-Real) dataset
bash scripts/download_data.sh nerf_llff
# NeRF-360 dataset
bash scripts/download_data.sh nerf_real_360
# Tanks and Temples dataset
bash scripts/download_data.sh tanks_and_temples
# LF dataset
bash scripts/download_data.sh lf
# NeRF-360-v2 dataset
bash scripts/download_data.sh nerf_360_v2
# Shiny-blender dataset
bash scripts/download_data.sh shiny_blender

Run the Code!

A very simple script to run the code.

Training Code

A script for running the training code.

python3 run.py --ginc configs/[model]/[data].gin --scene [scene]

## ex) run training nerf on chair scene of blender dataset
python3 run.py --ginc configs/nerf/blender.gin --scene chair

Evaluation Code

A script for running the evaluation code only.

python3 run.py --ginc configs/[model]/[data].gin --scene [scene] \
--ginb run.run_train=False

## ex) run evaluating nerf on chair scene of blender dataset
python3 run.py --ginc configs/nerf/blender.gin --scene chair \
--ginb run.run_train=False

Custom

How to add the custom dataset and the custom model in NeRF-Factory?

Custom Dataset

  • Add files of the custom dataset on ./data/[custom_dataset].
  • Implement a dataset loader code on ./src/data/data_util/[custom_dataset].py.
  • Implement a custom dataset class LitData[custom_dataset] on ./src/data/litdata.py.
  • Add option of selecting the custom dataset on the function def select_dataset() of ./utils/select_option.py.
  • Add gin config file for each model as ./configs/[model]/[custom_dataset].gin.

Custom Model

  • Implement a custom model code on ./src/model/[custom_model]/model.py.
  • Implement a custom model's helper code on ./src/model/[custom_model]/helper.py.
  • [Optional] If you need more code files for the custom model, you can add them in ./src/model/[custom_model]/.- Add option of selecting the custom model on the function def select_model() of ./utils/select_option.py.
  • Add gin config file for each model as ./configs/[custom_model]/[dataset].gin.

License

Copyright (c) 2022 POSTECH, KAIST, and Kakao Brain Corp. All Rights Reserved. Licensed under the Apache License, Version 2.0 (see LICENSE for details)

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