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https://hdl.handle.net/2440/124480
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Type: | Conference paper |
Title: | Self-supervised learning for single view depth and surface normal estimation |
Author: | Zhan, H. Weerasekera, C.S. Garg, R. Reid, I. |
Citation: | IEEE International Conference on Robotics and Automation, 2019 / Howard, A., Althoefer, K., Arai, F., Arrichiello, F., Caputo, B., Castellanos, J., Hauser, K., Isler, V., Kim, J., Liu, H., Oh, P., Santos, V., Scaramuzza, D., Ude, A., Voyles, R., Yamane, K., Okamura, A. (ed./s), vol.2019-May, pp.4811-4817 |
Publisher: | IEEE |
Publisher Place: | Piscataway, NJ. |
Issue Date: | 2019 |
Series/Report no.: | IEEE International Conference on Robotics and Automation ICRA |
ISBN: | 153866027X 9781538660263 |
ISSN: | 1050-4729 2577-087X |
Conference Name: | IEEE International Conference on Robotics and Automation (ICRA) (20 May 2019 - 24 May 2019 : Montreal, Canada) |
Editor: | Howard, A. Althoefer, K. Arai, F. Arrichiello, F. Caputo, B. Castellanos, J. Hauser, K. Isler, V. Kim, J. Liu, H. Oh, P. Santos, V. Scaramuzza, D. Ude, A. Voyles, R. Yamane, K. Okamura, A. |
Statement of Responsibility: | Huangying Zhan, Chamara Saroj Weerasekera, Ravi Garg, Ian Reid |
Abstract: | In this work we present a self-supervised learning framework to simultaneously train two Convolutional Neural Networks (CNNs) to predict depth and surface normals from a single image. In contrast to most existing frameworks which represent outdoor scenes as fronto-parallel planes at piecewise smooth depth, we propose to predict depth with surface orientation while assuming that natural scenes have piece-wise smooth normals. We show that a simple depth-normal consistency as a soft-constraint on the predictions is sufficient and effective for training both these networks simultaneously. The trained normal network provides state-of-the-art predictions while the depth network, relying on much realistic smooth normal assumption, outperforms the traditional self-supervised depth prediction network by a large margin on the KITTI benchmark. |
Rights: | ©2019 IEEE |
DOI: | 10.1109/ICRA.2019.8793984 |
Grant ID: | http://purl.org/au-research/grants/arc/FL130100102 http://purl.org/au-research/grants/arc/CE140100016 |
Published version: | https://ieeexplore.ieee.org/xpl/conhome/8780387/proceeding |
Appears in Collections: | Aurora harvest 4 Computer Science publications |
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