Scipediacontent at 21:33, 28 January 2021

2021-01-28T21:33:39Z

Scipediacontent: Scipediacontent moved page Draft Content 685931049 to Leroy et al 2018a

2020-10-14T14:17:15Z

Scipediacontent moved page Draft Content 685931049 to Leroy et al 2018a

Scipediacontent: Created page with " == Abstract == International audience; The rise of virtual and augmented reality fuels an increased need for contents suitable to these new technologies including 3D content..."

2020-10-14T14:17:12Z

Created page with " == Abstract == International audience; The rise of virtual and augmented reality fuels an increased need for contents suitable to these new technologies including 3D content..."

New page

== Abstract ==

International audience; The rise of virtual and augmented reality fuels an increased need for contents suitable to these new technologies including 3D contents obtained from real scenes. We consider in this paper the problem of 3D shape reconstruction from multi-view RGB images. We investigate the ability of learning-based strategies to effectively benefit the reconstruction of arbitrary shapes with improved precision and robustness. We especially target real life performance capture, containing complex surface details that are difficult to recover with existing approaches. A key step in the multi-view reconstruction pipeline lies in the search for matching features between viewpoints in order to infer depth information. We propose to cast the matching on a 3D receptive field along viewing lines and to learn a multi-view photoconsistency measure for that purpose. The intuition is that deep networks have the ability to learn local photometric configurations in a broad way, even with respect to different orientations along various viewing lines of the same surface point. Our results demonstrate this ability, showing that a CNN, trained on a standard static dataset, can help recover surface details on dynamic scenes that are not perceived by traditional 2D feature based methods. Our evaluation also shows that our solution compares on par to state of the art reconstruction pipelines on standard evaluation datasets, while yielding significantly better results and generalization with realistic performance capture data.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_685931049-beopen975-9178-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [https://hal.archives-ouvertes.fr/hal-01849286/file/1217.pdf https://hal.archives-ouvertes.fr/hal-01849286/file/1217.pdf]

← Older revision		Revision as of 21:33, 28 January 2021
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	International audience; The rise of virtual and augmented reality fuels an increased need for contents suitable to these new technologies including 3D contents obtained from real scenes. We consider in this paper the problem of 3D shape reconstruction from multi-view RGB images. We investigate the ability of learning-based strategies to effectively benefit the reconstruction of arbitrary shapes with improved precision and robustness. We especially target real life performance capture, containing complex surface details that are difficult to recover with existing approaches. A key step in the multi-view reconstruction pipeline lies in the search for matching features between viewpoints in order to infer depth information. We propose to cast the matching on a 3D receptive field along viewing lines and to learn a multi-view photoconsistency measure for that purpose. The intuition is that deep networks have the ability to learn local photometric configurations in a broad way, even with respect to different orientations along various viewing lines of the same surface point. Our results demonstrate this ability, showing that a CNN, trained on a standard static dataset, can help recover surface details on dynamic scenes that are not perceived by traditional 2D feature based methods. Our evaluation also shows that our solution compares on par to state of the art reconstruction pipelines on standard evaluation datasets, while yielding significantly better results and generalization with realistic performance capture data.		International audience; The rise of virtual and augmented reality fuels an increased need for contents suitable to these new technologies including 3D contents obtained from real scenes. We consider in this paper the problem of 3D shape reconstruction from multi-view RGB images. We investigate the ability of learning-based strategies to effectively benefit the reconstruction of arbitrary shapes with improved precision and robustness. We especially target real life performance capture, containing complex surface details that are difficult to recover with existing approaches. A key step in the multi-view reconstruction pipeline lies in the search for matching features between viewpoints in order to infer depth information. We propose to cast the matching on a 3D receptive field along viewing lines and to learn a multi-view photoconsistency measure for that purpose. The intuition is that deep networks have the ability to learn local photometric configurations in a broad way, even with respect to different orientations along various viewing lines of the same surface point. Our results demonstrate this ability, showing that a CNN, trained on a standard static dataset, can help recover surface details on dynamic scenes that are not perceived by traditional 2D feature based methods. Our evaluation also shows that our solution compares on par to state of the art reconstruction pipelines on standard evaluation datasets, while yielding significantly better results and generalization with realistic performance capture data.
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−	~~Document type: Part of book or chapter of book~~
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−	~~== Full document ==~~
−	~~<pdf>Media:Draft_Content_685931049-beopen975-9178-document.pdf</pdf>~~


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	* [https://hal.archives-ouvertes.fr/hal-01849286/file/1217.pdf https://hal.archives-ouvertes.fr/hal-01849286/file/1217.pdf]		* [https://hal.archives-ouvertes.fr/hal-01849286/file/1217.pdf https://hal.archives-ouvertes.fr/hal-01849286/file/1217.pdf]
		+
		+	* [http://link.springer.com/content/pdf/10.1007/978-3-030-01240-3_48 http://link.springer.com/content/pdf/10.1007/978-3-030-01240-3_48],
		+	: [http://dx.doi.org/10.1007/978-3-030-01240-3_48 http://dx.doi.org/10.1007/978-3-030-01240-3_48] under the license http://www.springer.com/tdm
		+
		+	* [http://openaccess.thecvf.com/content_ECCV_2018/papers/Vincent_Leroy_Shape_Reconstruction_Using_ECCV_2018_paper.pdf http://openaccess.thecvf.com/content_ECCV_2018/papers/Vincent_Leroy_Shape_Reconstruction_Using_ECCV_2018_paper.pdf],
		+	: [https://dblp.uni-trier.de/db/conf/eccv/eccv2018-9.html#LeroyFB18 https://dblp.uni-trier.de/db/conf/eccv/eccv2018-9.html#LeroyFB18],
		+	: [https://link.springer.com/chapter/10.1007/978-3-030-01240-3_48 https://link.springer.com/chapter/10.1007/978-3-030-01240-3_48],
		+	: [https://hal.archives-ouvertes.fr/hal-01849286v2 https://hal.archives-ouvertes.fr/hal-01849286v2],
		+	: [http://openaccess.thecvf.com/content_ECCV_2018/html/Vincent_Leroy_Shape_Reconstruction_Using_ECCV_2018_paper.html http://openaccess.thecvf.com/content_ECCV_2018/html/Vincent_Leroy_Shape_Reconstruction_Using_ECCV_2018_paper.html],
		+	: [https://www.scipedia.com/public/Leroy_et_al_2018a https://www.scipedia.com/public/Leroy_et_al_2018a],
		+	: [https://hal.archives-ouvertes.fr/hal-01849286/document https://hal.archives-ouvertes.fr/hal-01849286/document],
		+	: [https://eccv2018.org/openaccess/content_ECCV_2018/html/Vincent_Leroy_Shape_Reconstruction_Using_ECCV_2018_paper.html https://eccv2018.org/openaccess/content_ECCV_2018/html/Vincent_Leroy_Shape_Reconstruction_Using_ECCV_2018_paper.html],
		+	: [https://rd.springer.com/chapter/10.1007/978-3-030-01240-3_48 https://rd.springer.com/chapter/10.1007/978-3-030-01240-3_48],
		+	: [https://academic.microsoft.com/#/detail/2884556139 https://academic.microsoft.com/#/detail/2884556139]
		+
		+	* [https://hal.archives-ouvertes.fr/hal-01849286 https://hal.archives-ouvertes.fr/hal-01849286],
		+	: [https://hal.archives-ouvertes.fr/hal-01849286/document https://hal.archives-ouvertes.fr/hal-01849286/document],
		+	: [https://hal.archives-ouvertes.fr/hal-01849286/file/1217.pdf https://hal.archives-ouvertes.fr/hal-01849286/file/1217.pdf]
		+
		+	* [https://hal.archives-ouvertes.fr/hal-01849286 https://hal.archives-ouvertes.fr/hal-01849286],
		+	: [https://hal.archives-ouvertes.fr/hal-01849286v2/document https://hal.archives-ouvertes.fr/hal-01849286v2/document],
		+	: [https://hal.archives-ouvertes.fr/hal-01849286/file/1217.pdf https://hal.archives-ouvertes.fr/hal-01849286/file/1217.pdf]
		+
		+	* [https://hal.archives-ouvertes.fr/hal-01849286 https://hal.archives-ouvertes.fr/hal-01849286],
		+	: [https://hal.archives-ouvertes.fr/hal-01849286v2/document https://hal.archives-ouvertes.fr/hal-01849286v2/document],
		+	: [https://hal.archives-ouvertes.fr/hal-01849286v2/file/1217.pdf https://hal.archives-ouvertes.fr/hal-01849286v2/file/1217.pdf]

← Older revision	Revision as of 14:17, 14 October 2020
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Leroy et al 2018a - Revision history

Scipediacontent at 21:33, 28 January 2021

Scipediacontent: Scipediacontent moved page Draft Content 685931049 to Leroy et al 2018a

Scipediacontent: Created page with " == Abstract == International audience; The rise of virtual and augmented reality fuels an increased need for contents suitable to these new technologies including 3D content..."