Aleth-NeRF: Illumination Adaptive NeRF with Concealing Field Assumption
Authors: Ziteng Cui, Lin Gu, Xiao Sun, Xianzheng Ma, Yu Qiao, Tatsuya Harada
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we present a comprehensive multi-view dataset captured under challenging illumination conditions for evaluation. Our code and proposed dataset are available at https://github.com/cuiziteng/Aleth-Ne RF. Extensive experiments show that our Aleth-Ne RF achieves satisfactory enhancement quality and multi-view consistency. Our evaluation metrics include PSNR (P) , SSIM (S) and LPIPS (L). |
| Researcher Affiliation | Collaboration | Ziteng Cui1,2, Lin Gu3,1, Xiao Sun2*, Xianzheng Ma4, Yu Qiao2, Tatsuya Harada1,3 1The University of Tokyo 2Shanghai AI Laboratory 3RIKEN AIP 4University of Oxford |
| Pseudocode | No | The paper describes the approach using mathematical equations and descriptive text but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and proposed dataset are available at https://github.com/cuiziteng/Aleth-Ne RF. |
| Open Datasets | Yes | We contribute a challenging illumination multi-view dataset, with paired s RGB low-light & normal-light & over-exposure images, dataset would also be public. Our code and proposed dataset are available at https://github.com/cuiziteng/Aleth-Ne RF. In our proposed LOM dataset, we collected 5 scenes ( buu , chair , sofa , bike , shrub ) in real-world. |
| Dataset Splits | Yes | For dataset split, in each scene, we choose 3 5 images as the testing set, 1 image as the validation set, and other images to be the training set, details of training and evaluation views split is shown in Table. 1. Table 1: Details of the dataset split for LOM. scene buu chair sofa bike shrub collected views 25 48 33 40 35 training views 22 43 29 36 30 evaluation views 3 5 4 4 5 |
| Hardware Specification | No | The paper does not specify any particular hardware details such as GPU models, CPU models, or memory used for experiments. |
| Software Dependencies | No | The paper mentions building the framework on 'the open-source Py Torch toolbox Ne RF-Factory', but it does not specify version numbers for PyTorch or any other libraries. |
| Experiment Setup | Yes | We utilize the Adam optimizer with an initial learning rate of 5e 4 and employ a cosine learning rate decay strategy every 2500 iterations. The training batch size is set at 4096 for a total of 62500 iterations. The overall training loss is then represented as: L = Lit mse + λ1 Lde + λ2 Lco + λ3 Lcc, where λ1, λ2 and λ3 are three non-negative parameters to balance total loss weights, which we set to 1e 3, 1e 3 and 1e 8 respectively. |