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| Presentation | 2021-03-05 14:40 Learning from Synthetic Shadows Naoto Inoue, Toshihiko Yamasaki (UTokyo)  PRMU2020-92 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Shadow removal is essential for some downstream tasks in computer vision and computer graphics. Recent shadow removal approaches all train convolutional neural networks (CNN) on real paired shadow/shadow-free/mask image datasets. However, obtaining a large-scale, diverse, and accurate dataset has been a primary challenge. It limits the generalization performance of the learned models on shadow images with unseen shapes/intensities. We present SynShadow, a novel large-scale synthetic shadow/shadow-free/matte image triplets dataset and a pipeline to synthesize it to tackle this challenge. We extend a physically-grounded shadow illumination model and synthesize a shadow image given an arbitrary combination of a shadow-free image, a matte image, and shadow attenuation parameters. This pipeline enables us to sample a countless number of the triplets. SynShadow offers a dataset with high fidelity and diversity. We demonstrate that shadow removal models trained on SynShadow perform favorably in removing shadows with various shapes and intensities. Furthermore, we show that simply fine-tuning from a SynShadow-pre-trained model improves existing shadow detection and removal models. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | shadow removal / shadow detection / CNN / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 409, PRMU2020-92, pp. 133-138, March 2021. | |
| Paper # | PRMU2020-92 | |
| Date of Issue | 2021-02-25 (PRMU) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | PRMU2020-92 |
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| Conference Information | |
| Committee | PRMU IPSJ-CVIM |
| Conference Date | 2021-03-04 - 2021-03-05 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Computer Vision and Pattern Recognition for specific environment |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2021-03-PRMU-CVIM |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Learning from Synthetic Shadows |
| Sub Title (in English) | |
| Keyword(1) | shadow removal |
| Keyword(2) | shadow detection |
| Keyword(3) | CNN |
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| 1st Author's Name | Naoto Inoue |
| 1st Author's Affiliation | The University of Tokyo (UTokyo) |
| 2nd Author's Name | Toshihiko Yamasaki |
| 2nd Author's Affiliation | The University of Tokyo (UTokyo) |
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| Speaker | Author-1 |
| Date Time | 2021-03-05 14:40:00 |
| Presentation Time | 15 minutes |
| Registration for | PRMU |
| Paper # | PRMU2020-92 |
| Volume (vol) | vol.120 |
| Number (no) | no.409 |
| Page | pp.133-138 |
| #Pages | 6 |
| Date of Issue | 2021-02-25 (PRMU) |
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