RobIR: Robust Inverse Rendering for High-Illumination Scenes
Authors: Ziyi Yang, Chenyanzhen , Xinyu Gao, YazhenYuan , Wu Yu, Xiaowei Zhou, Xiaogang Jin
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the experimental evaluation of our methods. To assess the effectiveness of our approach, we collect synthetic and real-world datasets from Ne RF and Neu S without any post-processing. In addition, we use Blender to render our own datasets to further demonstrate the superiority of our methods in high-illumination scenes. ... Tab. 1 shows the accuracy of the albedo, roughness, relighting, and environment map averaged over synthetic scenes. ... We can observe that our method achieve the best results in all inverse rendering tasks. |
| Researcher Affiliation | Collaboration | Ziyi Yang1 Yanzhen Chen1 Xinyu Gao1 Yazhen Yuan2 Yu Wu2 Xiaowei Zhou1 Xiaogang Jin1 1State Key Lab of CAD&CG, Zhejiang University 2Tencent |
| Pseudocode | No | The paper describes its methods in prose and with diagrams (e.g., Figure 1), but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/ingra14m/Rob IR. |
| Open Datasets | Yes | To assess the effectiveness of our approach, we collect synthetic and real-world datasets from Ne RF and Neu S without any post-processing. In addition, we use Blender to render our own datasets to further demonstrate the superiority of our methods in high-illumination scenes. It should be noted that unlike previous methods [17, 55] that used a hotdog scene with reduced illumination, we use the original hotdog from Ne RF [32] without reduced illumination. |
| Dataset Splits | No | The paper mentions 'batch size of 1024, with 200k iterations for the Neu S training' and discusses training and test results on synthetic and real-world datasets, but it does not specify explicit training/validation/test splits (e.g., percentages or counts) or reference predefined splits with citations for reproducibility. |
| Hardware Specification | Yes | All tests were conducted on a single Tesla V100 GPU with 32GB memory. |
| Software Dependencies | No | The paper states 'The model was implemented in Py Torch and optimized with the Adam optimizer', but it does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Our model hyperparameters consisted of a batch size of 1024, with 200k iterations for the Neu S training. The model was implemented in Py Torch and optimized with the Adam optimizer at a learning rate of 5e 4. |