Ziteng Cui1,2, Lin Gu3,1, Xiao Sun2*, Xianzheng Ma4, Yu Qiao2, Tatsuya Harada1,3.
1.The University of Tokyo, 2.Shanghai AI Lab, 3.RIKEN AIP, 4.University of Oxford
2024.1.25 : Update the renewed experimental results, dataset and arxiv version. We now provide all the comparision results and you can feel free to make comparision on your own research. You can download the experimental results of Aleth-NeRF and comparison methods, Low-Light-Results from (google drive) or (baiduyun (passwd: 729w)), and Over-Exposure Results from (google drive) or (baiduyun (passwd: 6q4k)).
2023.12.9 : Paper accepted by AAAI 2024 ! The old version of our paper is invalid, please refer to the new version, thanks~
" Can you see your days blighted by darkness ? "
-- Pink Floyd (Lost For Words)
Acknowledgement:
Code is based on NeRF-Factory, much thanks to their excellent codebase! Also if you use our dataset or our code & paper help you, please consider cite our AAAI version:
@inproceedings{cui_aleth_nerf,
title={Aleth-NeRF: Illumination Adaptive NeRF with Concealing Field Assumption},
author={Cui, Ziteng and Gu, Lin and Sun, Xiao and Ma, Xianzheng and Qiao, Yu and Harada, Tatsuya},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2024}
}
The standard Neural Radiance Fields (NeRF) paradigm employs a viewer-centered methodology, entangling the aspects of illumination and material reflectance into emission solely from 3D points. This simplified rendering approach presents challenges in accurately modeling images captured under adverse lighting conditions, such as low light or over-exposure. Motivated by the ancient Greek emission theory that posits visual perception as a result of rays emanating from the eyes, we slightly refine the conventional NeRF framework to train NeRF under challenging light conditions and generate normal-light condition novel views unsupervised. We introduce the concept of a ”Concealing Field,” which assigns transmittance values to the surrounding air to account for illumination effects. In dark scenarios, we assume that object emissions maintain a standard lighting level but are attenuated as they traverse the air during the rendering process. Concealing Field thus compel NeRF to learn reasonable density and colour estimations for objects even in dimly lit situations. Similarly, the Concealing Field can mitigate over-exposed emissions during the rendering stage. Furthermore, we present a comprehensive multi-view dataset captured under challenging illumination conditions for evaluation.
We assume objects are naturally visible. However, the Concealing Field attenuates the light in the viewing direction, making the people see a low-light scene. (c). Remove the concealing field, we can render out normal-light images in low-light scenes. (d). Add the concealing field, we can render out normal-light in over-exposure scenes.
1.
$ git clone https://github.com/cuiziteng/Aleth-NeRF.git
$ cd Aleth-NeRF
2. (You can adjust to your own torch>1.8.0 version and CUDA version)
$ conda create -n aleth_nerf -c anaconda python=3.8
$ conda activate aleth_nerf
$ conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch
$ pip3 install -r requirements.txt
We collect the first paired low-light & normal-light & over-exposure multi-view images dataset. Download the LOM dataset from: [google drive] or [baiduyun (passwd: ve1t)].
LOM dataset contains 5 scenes (buu | chair | sofa | bike | shrub), each scene includes 25~65 paired multi-view normal-light & low-light images & over-exposure images, and low-light images enhanced by different 2D low-light enhancement methods.
Unzip the download file, place LOM under
data
└───
LOM_full
└─── buu
│─── colmap_sparse
│─── colmap_text
│─── high (normal-light images)
│─── low (low-light images)
│─── over_exp (over-exposure images)
│─── Low_light_enhance (low-light images enhanced by 2D enhance methods)
│─── enh_RetiNexNet (enhanced by [RetiNexNet, BMVC 2018])
│─── enh_zerodce (enhanced by [Zero-DCE, CVPR 2020])
│─── enh_SCI (enhanced by [SCI, CVPR 2022])
│─── enh_IAT (enhanced by [IAT, BMVC 2022])
│─── enh_MBLLEN (enhanced by video enhance method [MBLLEN, BMVC 2018])
│─── enh_LLVE (enhanced by video enhance method [LLVE, CVPR 2021])
│─── Exposure_correction (over-exp images corrected by 2D exposure correction methods)
│─── HE (corrected by Histogram Equlization)
│─── IAT (corrected by [IAT, BMVC 2022])
│─── MSEC (corrected by [MSEC, CVPR 2021])
│─── colamp.db
│─── transforms_test.json (test scenes)
│─── transforms_train.json (train scenes)
│─── transforms_val.json (validation scenes)
│─── chair
│─── ...
│─── sofa
│─── ...
│─── bike
│─── ...
│─── shrub
│─── ...
By default, we use 4 GPUs to train Aleth-NeRF on LOM dataset (around 2 hours ~ 2.5 hours per scene), you can feel free to set other GPU number or GPU id depend on your own device. We take "buu" scene training for example:
For low-light conditions, we default set con = 12 and eta = 0.45 (Table.2's results):
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 run.py --ginc configs/LOM/aleth_nerf/aleth_nerf_buu.gin --logbase ./logs --con 12 --eta 0.45
You can also adjust the hyper-parameter "con" (contrast degree) and "eta" (enhance degree) to achieve different enhance results, like:
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 run.py --ginc configs/LOM/aleth_nerf/aleth_nerf_buu.gin --logbase ./logs --con 10/12/15 --eta 0.4/0.45/0.5
For over-exposure conditions, we default set con = 1 and eta = 0.45 (Table.3's results):
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 run.py --ginc configs/LOM/aleth_nerf_exp/aleth_nerf_buu.gin --logbase ./logs_exp --con 1 --eta 0.45
You can also direct use following command to run all 5 scenes scenes together:
bash run/run_LOM_aleth.sh
You could also download our pre-train weights for direct model evaluation Low-Light-Results from (google drive) or (baiduyun (passwd: 729w)), and Over-Exposure Results from (google drive) or (baiduyun (passwd: 6q4k)), then unzip the file under this folder (
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 run.py --ginc configs/LOM/aleth_nerf/aleth_nerf_buu.gin --con 12 --eta 0.45 --logbase ./logs --ginb run.run_train=False
if you want to render out videos with novel views, direct add "--ginb run.run_render=True":
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 run.py --ginc configs/LOM/aleth_nerf/aleth_nerf_buu.gin --con 12 --eta 0.45 --logbase ./logs --ginb run.run_train=False --ginb run.run_render=True
You can also direct use following command to render all 5 scenes scenes together:
bash run/run_LOM_aleth_test.sh
All the comparision methods' weights and experimental results could be downloaded, Low-Light-Results from (google drive) or (baiduyun (passwd: 729w)), and Over-Exposure Results from (google drive) or (baiduyun (passwd: 6q4k)). We formulate the comparision results as follow:
Low-Light-Results :
logs
└───
└─── Aleth-NeRF (Aleth-NeRF results with various ablation)
└─── NeRF (NeRF results and "NeRF + 2D enhance methods" Results)
└─── RetiNexNet (RetiNexNet + NeRF)
└─── SCI (SCI + NeRF)
└─── zerodce (zerodce + NeRF)
└─── IAT (IAT+ NeRF)
└─── MBLLEN (MBLLEN+ NeRF)
└─── LLVE (LLVE+ NeRF)
## LOM dataset low-light benchmark ##
Over-Exposure-Results :
logs
└───
└─── Aleth-NeRF (Aleth-NeRF results)
└─── NeRF (NeRF results and "NeRF + 2D exposure correction" Results)
└─── HE (Histogram Equlization + NeRF)
└─── IAT (IAT + NeRF)
└─── MSEC (MSEC + NeRF)
## LOM dataset over-exposure benchmark ##
Then you can evlaution with our pre-train weights.
For basic NeRF methods:
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 run.py --ginc configs/LOM/nerf/nerf_buu.gin --ginb run.run_train=False
For NeRF trained on enhanced images:
CUDA_VISIBLE_DEVICES=0,1,2,3 python3 run.py --ginc configs/LOM/compare_methods/RetiNexNet/nerf_buu.gin --ginb run.run_train=False
If you want to editing the code or find out details of Aleth-NeRF, direct refer to model.py and helper.py.
For the angle control to render a video in LOM dataset, please refer to here.