Intrinsic Image Decomposition by Pursuing Reflectance Image
Authors: Tzu-Heng Lin, Pengxiao Wang, Yizhou Wang
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that our proposed network outperforms current state-of-the-art results by a large margin on the most challenging real-world IIW dataset. We also surprisingly find that on the densely labeled datasets (MIT and MPI-Sintel), our network can also achieve state-of-the-art results on both reflectance and shading images, when we only apply supervision on the reflectance images during training. |
| Researcher Affiliation | Academia | Tzu-Heng Lin1 , Pengxiao Wang1 and Yizhou Wang1,2 1 School of Computer Science, Peking University 2 Center on Frontiers of Computing Studies, Peking University |
| Pseudocode | No | The paper describes network components and their functionality but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available in https://github.com/lzhbrian/RDNet |
| Open Datasets | Yes | Sparsely Labeled Dataset IIW dataset. The real-world IIW dataset [Bell et al., 2014] contains 872,161 pairwise reflectance comparisons across 5,230 photos. Densely Labeled Datasets MPI-Sintel dataset. The MPI-Sintel dataset [Butler et al., 2012] contains 8950 images from 18 scene level computer generated images sequences. MIT dataset. The MIT dataset [Grosse et al., 2009] contains 20 object level images, each with 11 different lighting conditions. |
| Dataset Splits | No | The paper mentions using training and test sets but does not explicitly provide details for a validation split (e.g., percentages or sample counts). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for experiments are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch versions) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the loss functions used for training but does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, optimizer type, number of epochs). |