Omnidirectional Image Super-resolution via Bi-projection Fusion
Authors: Jiangang Wang , Yuning Cui, Yawen Li, Wenqi Ren, Xiaochun Cao
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that BPOSR achieves state-of-the-art performance on omnidirectional image super-resolution. Experiments Dataset and Implementation Details We verify the effectiveness of our method using the widely used datasets: ODI-SR (Deng et al. 2021) and SUN360 (Xiao et al. 2012), which contain various types of panoramic scenes. |
| Researcher Affiliation | Academia | Jiangang Wang1, Yuning Cui2, Yawen Li3, Wenqi Ren1*, Xiaochun Cao1 1Shenzhen Campus of Sun Yat-sen University 2Technical University of Munich 3Beijing University of Posts and Telecommunications |
| Pseudocode | No | The paper describes the overall architecture and components like HSTB, PSTB, and BAFM, and illustrates them with diagrams, but it does not include formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/W-JG/BPOSR. |
| Open Datasets | Yes | We verify the effectiveness of our method using the widely used datasets: ODI-SR (Deng et al. 2021) and SUN360 (Xiao et al. 2012), which contain various types of panoramic scenes. |
| Dataset Splits | No | The paper mentions training and test sets ("The model is trained using 1200 training images of ODI-SR and evaluated on the test sets of ODI-SR and SUN360, both containing 100 images."), but does not specify a separate validation set split or its size/details. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used, such as GPU or CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not list specific version numbers for software dependencies or libraries used in the implementation. |
| Experiment Setup | Yes | The model is trained for 500k iterations with the initial learning rate as 2 10 4, which is halved at 250k, 400k, 450k, and 475k iterations. In our model, K is set to 4, and the number of STL and HSTL is both set to 6. The attention window sizes of HSTB and PSTB are set as 4 16 and 8 8, respectively. The model feature dimension is set to 60, and the rotation magnification in PSTB is set to 3 times. |