LuSh-NeRF: Lighting up and Sharpening NeRFs for Low-light Scenes
Authors: Zefan Qu, Ke Xu, Gerhard Hancke, Rynson Lau
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that Lu Sh-Ne RF outperforms existing approaches. We construct a new dataset containing both synthetic and real images. Experiments show that Lu Sh-Ne RF outperforms existing approaches. PSNR, SSIM, and LPIPS [63] metrics are used to evaluate the performance difference... Ablation Study. Fig. 6 demonstrates the effect of the various components of Lu Sh-Ne RF on a realistic scenario. We conduct a quantitative comparison of our method against various combinations of SOTA approaches on our synthesized data in Tab. 1. |
| Researcher Affiliation | Academia | Zefan Qu Ke Xu Gerhard Petrus Hancke Rynson W.H. Lau Department of Computer Science City University of Hong Kong zefanqu2-c@my.cityu.edu.hk, kkangwing@gmail.com, {gp.hancke, Rynson.Lau}@cityu.edu.hk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks explicitly labeled as 'Algorithm' or 'Pseudocode'. |
| Open Source Code | Yes | Our code and dataset can be found here: https://github.com/quzefan/Lu Sh-Ne RF. |
| Open Datasets | Yes | To facilitate training and evaluations, we construct a new dataset containing both synthetic and real images. Our code and dataset can be found here: https://github.com/quzefan/Lu Sh-Ne RF. Since we are the first to reconstruct Ne RF with hand-held low-light photographs, we build a new dataset based on the low-light image deblur dataset [67] for training and evaluation. Specifically, our dataset consists of 5 synthetic and 5 real scenes, for the quantitative and generalization evaluations. We use the COLMAP [42] method to estimate the camera pose of each image in the scenarios. |
| Dataset Splits | Yes | Table 2: The dataset split details for our proposed LOL-Blur Ne RF dataset. ... Scenario ... Collected Views ... Training Views ... Evaluation Views. This table clearly shows the number of training and evaluation views for each scene. |
| Hardware Specification | Yes | All the experiments in this paper are performed on a PC with an i9-13900K CPU and a single NVIDIA RTX3090 GPU. |
| Software Dependencies | No | The paper mentions implementing based on 'official code of Deblur-Ne RF [26]' and using 'Rigid Blurring Kernel network in [19]', and uses 'COLMAP [42]' and 'GIM [43]', but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The number of camera motions k and the frequency filter radius in the CTP module are set to 4 and 30. The number of aligned rays K and certainty threshold θ in the SND module are set to 20 and 0.8. Before training, the input images are up-scaled by gamma adjustment and histogram equalization. The batch size is set to 1,024 rays, with 64 fine and coarse sampled points. α and β are set to 1 and 0 during the first 60K iterations for better rendering results, to avoid the inaccuracy matching matrix M interfering with the SND module. The two hyper-parameters are then changed to 1 and 1 10 2 in the last 40K iterations. |