Open AccessPosted Content
AIM 2020: Scene Relighting and Illumination Estimation Challenge
Majed El Helou,Ruofan Zhou,Sabine Süsstrunk,Radu Timofte,Mahmoud Afifi,Michael S. Brown,Kele Xu,Hengxing Cai,Yuzhong Liu,Li-Wen Wang,Zhi-Song Liu,Chu-Tak Li,Sourya Dipta Das,Nisarg Shah,Akashdeep Jassal,Tongtong Zhao,Shanshan Zhao,Sabari Nathan,M. Parisa Beham,R. Suganya,Qing Wang,Zhongyun Hu,Xin Huang,Yaning Li,Maitreya Suin,Kuldeep Purohit,A. N. Rajagopalan,Densen Puthussery,Hrishikesh P S,Melvin Kuriakose,C. V. Jiji,Yu Zhu,Liping Dong,Zhuolong Jiang,Chenghua Li,Cong Leng,Jian Cheng +36 more
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TLDR
The novel VIDIT dataset used in the AIM 2020 challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks are presented.Abstract:
We review the AIM 2020 challenge on virtual image relighting and illumination estimation. This paper presents the novel VIDIT dataset used in the challenge and the different proposed solutions and final evaluation results over the 3 challenge tracks. The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i.e., light source position). The goal of the second track was to estimate illumination settings, namely the color temperature and orientation, from a given image. Lastly, the third track dealt with any-to-any relighting, thus a generalization of the first track. The target color temperature and orientation, rather than being pre-determined, are instead given by a guide image. Participants were allowed to make use of their track 1 and 2 solutions for track 3. The tracks had 94, 52, and 56 registered participants, respectively, leading to 20 confirmed submissions in the final competition stage.read more
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Book ChapterDOI
AIM 2020: Scene Relighting and Illumination Estimation Challenge
Majed El Helou,Ruofan Zhou,Sabine Süsstrunk,Radu Timofte,Mahmoud Afifi,Michael S. Brown,Kele Xu,Hengxing Cai,Yuzhong Liu,Li-Wen Wang,Zhi-Song Liu,Chu-Tak Li,Sourya Dipta Das,Nisarg Shah,Akashdeep Jassal,Tongtong Zhao,Shanshan Zhao,Sabari Nathan,M. Parisa Beham,R. Suganya,Qing Wang,Zhongyun Hu,Xin Huang,Yaning Li,Maitreya Suin,Kuldeep Purohit,A. N. Rajagopalan,Densen Puthussery,P. S. Hrishikesh,Melvin Kuriakose,C. V. Jiji,Yu Zhu,Liping Dong,Zhuolong Jiang,Chenghua Li,Cong Leng,Jian Cheng +36 more
TL;DR: The AIM 2020 challenge on virtual image relighting and illumination estimation as discussed by the authors focused on one-to-one relighting, where the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation.
References
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Posted Content
Multi-Task Learning as Multi-Objective Optimization
Ozan Sener,Vladlen Koltun +1 more
TL;DR: This paper cast multi-task learning as a multi-objective optimization problem, with the overall objective of finding a Pareto optimal solution, and propose an upper bound for the multiobjective loss and show that it can be optimized efficiently.
Journal ArticleDOI
Intrinsic images in the wild
TL;DR: This paper introduces Intrinsic Images in the Wild, a large-scale, public dataset for evaluating intrinsic image decompositions of indoor scenes, and develops a dense CRF-based intrinsic image algorithm for images in the wild that outperforms a range of state-of-the-art intrinsic image algorithms.
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Selective Kernel Networks
TL;DR: Detailed analyses show that the neurons in SKNet can capture target objects with different scales, which verifies the capability of neurons for adaptively adjusting their receptive field sizes according to the input.
Book ChapterDOI
Intrinsic Images by Entropy Minimization
TL;DR: This paper shows that not only does a correct shadow-free image emerge, but also that the angle found agrees with that recovered from a calibration, and can be applied successfully to remove shadows from unsourced imagery.
Journal ArticleDOI
User-assisted intrinsic images
TL;DR: A new approach is described that enables users to guide an optimization with simple indications such as regions of constant reflectance or illumination, and derives a new propagation energy that enables a closed form solution using linear least-squares.