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Conference Paper: PS-FCN: A Flexible Learning Framework for Photometric Stereo

TitlePS-FCN: A Flexible Learning Framework for Photometric Stereo
Authors
Issue Date2018
PublisherSpringer..
Citation
European Conference on Computer Vision (ECCV), Munich, Germany, 8-14 September 2018. In Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIII, p. 3-19 How to Cite?
AbstractThis paper addresses the problem of photometric stereo for non-Lambertian surfaces. Existing approaches often adopt simplified reflectance models to make the problem more tractable, but this greatly hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS-FCN, that takes an arbitrary number of images of a static object captured under different light directions with a fixed camera as input, and predicts a normal map of the object in a fast feed-forward pass. Unlike the recently proposed learning based method, PS-FCN does not require a pre-defined set of light directions during training and testing, and can handle multiple images and light directions in an order-agnostic manner. Although we train PS-FCN on synthetic data, it can generalize well on real datasets. We further show that PS-FCN can be easily extended to handle the problem of uncalibrated photometric stereo. Extensive experiments on public real datasets show that PS-FCN outperforms existing approaches in calibrated photometric stereo, and promising results are achieved in uncalibrated scenario, clearly demonstrating its effectiveness.
DescriptionPoster session 3A - no. P-3A-34
Persistent Identifierhttp://hdl.handle.net/10722/261167
ISSN

 

DC FieldValueLanguage
dc.contributor.authorChen, G-
dc.contributor.authorHan, K-
dc.contributor.authorWong, KKY-
dc.date.accessioned2018-09-14T08:53:35Z-
dc.date.available2018-09-14T08:53:35Z-
dc.date.issued2018-
dc.identifier.citationEuropean Conference on Computer Vision (ECCV), Munich, Germany, 8-14 September 2018. In Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XIII, p. 3-19-
dc.identifier.issn9783030012601-
dc.identifier.urihttp://hdl.handle.net/10722/261167-
dc.descriptionPoster session 3A - no. P-3A-34-
dc.description.abstractThis paper addresses the problem of photometric stereo for non-Lambertian surfaces. Existing approaches often adopt simplified reflectance models to make the problem more tractable, but this greatly hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS-FCN, that takes an arbitrary number of images of a static object captured under different light directions with a fixed camera as input, and predicts a normal map of the object in a fast feed-forward pass. Unlike the recently proposed learning based method, PS-FCN does not require a pre-defined set of light directions during training and testing, and can handle multiple images and light directions in an order-agnostic manner. Although we train PS-FCN on synthetic data, it can generalize well on real datasets. We further show that PS-FCN can be easily extended to handle the problem of uncalibrated photometric stereo. Extensive experiments on public real datasets show that PS-FCN outperforms existing approaches in calibrated photometric stereo, and promising results are achieved in uncalibrated scenario, clearly demonstrating its effectiveness.-
dc.languageeng-
dc.publisherSpringer.. -
dc.relation.ispartofEuropean Conference on Computer Vision (ECCV)-
dc.titlePS-FCN: A Flexible Learning Framework for Photometric Stereo-
dc.typeConference_Paper-
dc.identifier.emailWong, KKY: kykwong@cs.hku.hk-
dc.identifier.authorityWong, KKY=rp01393-
dc.description.naturepostprint-
dc.identifier.hkuros290259-
dc.identifier.spage3-
dc.identifier.epage19-
dc.publisher.placeCham, Switzerland-

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