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Towards Geometry Guided Neural Relighting with Flash Photography
2020 International Conference on 3D Vision (3DV)
Year: 2020 | Conference Paper |
Cited by: Papers (5)
Previous image based relighting methods require capturing multiple images to acquire high frequency lighting effect under different lighting conditions, which needs nontrivial effort and may be unrealistic in certain practical use scenarios. While such approaches rely entirely on cleverly sampling the color images under different lighting conditions, little has been done to utilize geometric infor...Show More
StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020 | Conference Paper |
Cited by: Papers (39)
Large-scale synthetic datasets are beneficial to stereo matching but usually introduce known domain bias. Although unsupervised image-to-image translation networks represented by CycleGAN show great potential in dealing with domain gap, it is non-trivial to generalize this method to stereo matching due to the problem of pixel distortion and stereo mismatch after translation. In this paper, we prop...Show More
AIM 2019 Challenge on Bokeh Effect Synthesis: Methods and Results
Andrey Ignatov;Jagruti Patel;Radu Timofte;Bolun Zheng;Xin Ye;Li Huang;Xiang Tian;Saikat Dutta;Kuldeep Purohit;Praveen Kandula;Maitreya Suin;A. N. Rajagopalan;Zhiwei Xiong;Jie Huang;Guanting Dong;Mingde Yao;Dong Liu;Ming Hong;Wenying Lin;Yanyun Qu;Jae-Seok Choi;Woonsung Park;Munchurl Kim;Rui Liu;Xiangyu Mao;Chengxi Yang;Qiong Yan;Wenxiu Sun;Junkai Fang;Meimei Shang;Fei Gao;Sujoy Ghosh;Prasen Kumar Sharma;Arijit Sur;Wenjin Yang
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019 | Conference Paper |
Cited by: Papers (30)
This paper reviews the first AIM challenge on bokeh effect synthesis with the focus on proposed solutions and results. The participating teams were solving a real-world image-to-image mapping problem, where the goal was to map standard narrow-aperture photos to the same photos captured with a shallow depth-of-field by the Canon 70D DSLR camera. In this task, the participants had to restore bokeh e...Show More
AIM 2019 Challenge on Bokeh Effect Synthesis: Methods and Results
Andrey Ignatov;Jagruti Patel;Radu Timofte;Bolun Zheng;Xin Ye;Li Huang;Xiang Tian;Saikat Dutta;Kuldeep Purohit;Praveen Kandula;Maitreya Suin;A. N. Rajagopalan;Zhiwei Xiong;Jie Huang;Guanting Dong;Mingde Yao;Dong Liu;Ming Hong;Wenying Lin;Yanyun Qu;Jae-Seok Choi;Woonsung Park;Munchurl Kim;Rui Liu;Xiangyu Mao;Chengxi Yang;Qiong Yan;Wenxiu Sun;Junkai Fang;Meimei Shang;Fei Gao;Sujoy Ghosh;Prasen Kumar Sharma;Arijit Sur;Wenjin Yang
Year: 2019 | Conference Paper |
Deep End-to-End Alignment and Refinement for Time-of-Flight RGB-D Module
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019 | Conference Paper |
Cited by: Papers (18)
Recently, it is increasingly popular to equip mobile RGB cameras with Time-of-Flight (ToF) sensors for active depth sensing. However, for off-the-shelf ToF sensors, one must tackle two problems in order to obtain high-quality depth with respect to the RGB camera, namely 1) online calibration and alignment; and 2) complicated error correction for ToF depth sensing. In this work, we propose a framew...Show More
Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domains
Jiahao Pang;Wenxiu Sun;Chengxi Yang;Jimmy Ren;Ruichao Xiao;Jin Zeng;Liang Lin
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018 | Conference Paper |
Cited by: Papers (38)
Despite the recent success of stereo matching with convolutional neural networks (CNNs), it remains arduous to generalize a pre-trained deep stereo model to a novel domain. A major difficulty is to collect accurate ground-truth disparities for stereo pairs in the target domain. In this work, we propose a self-adaptation approach for CNN training, utilizing both synthetic training data (with ground...Show More
Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching
2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
Year: 2017 | Conference Paper |
Cited by: Papers (339)
Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performance exceeding traditional approaches. However, it remains challenging to generate high-quality disparities for the inherently ill-posed regions. To tackle this problem, we propose a novel cascade CNN architecture composing...Show More
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