From Chaos to Clarity: 3DGS in the Dark
Authors: Zhihao Li, Yufei Wang, Alex Kot, Bihan Wen
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our method outperforms LDR/HDR 3DGS and previous state-of-the-art (SOTA) self-supervised and supervised pre-trained models in both reconstruction quality and inference speed on the Raw Ne RF dataset across a broad range of training views. |
| Researcher Affiliation | Academia | Zhihao Li Yufei Wang Alex Kot Bihan Wen Department of EEE, Nanyang Technology University, Singapore |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | Our code is under internal review and will be released upon paper acceptance. |
| Open Datasets | Yes | We utilize the Raw Ne RF dataset 3, which comprises raw images captured in dark scenes using an i Phone X with various ISO settings. 3https://bmild.github.io/rawnerf/ google-research. Licensed under the Apache License, v2.0. |
| Dataset Splits | Yes | For the full-views training setting, we adhere to the same train-test view splits as in Raw Ne RF. For limited views settings, we randomly select subsets (4, 8, 12, 16, 20 views) from the training views, while maintaining a consistent test view set across all experiments to ensure fair comparison. |
| Hardware Specification | Yes | All 3DGS models are trained on a single NVIDIA RTX 3090 GPU, following the default parameter settings of Scaffold-GS. |
| Software Dependencies | No | The paper mentions building upon 'Scaffold-GS [21]' and pretraining on the 'SID dataset [2]', but does not specify software dependencies with version numbers (e.g., PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | The hyperparameters λnd and λcov, as defined in Eq. (15) and Eq. (14), are set to 5 and 20 for full-views settings, and 3 and 20 for limited views settings, respectively. All 3DGS models are trained on a single NVIDIA RTX 3090 GPU, following the default parameter settings of Scaffold-GS. |