Skeleton-Based Conditionally Independent Gaussian Process Implicit Surfaces for Fusion in Sparse to Dense 3D Reconstruction

Wu, L; Falque, R; Perez-Puchalt, V; Liu, L; Pietroni, N; Vidal-Calleja, T

Skeleton-Based Conditionally Independent Gaussian Process Implicit Surfaces for Fusion in Sparse to Dense 3D Reconstruction

Wu, L Falque, R Perez-Puchalt, V Liu, L

Pietroni, N

Vidal-Calleja, T

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Robotics and Automation Letters, 2020, 5, (2), pp. 1532-1539
Issue Date:: 2020-04

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Field	Value	Language
dc.contributor.author	Wu, L
dc.contributor.author	Falque, R
dc.contributor.author	Perez-Puchalt, V
dc.contributor.author	Liu, L https://orcid.org/0000-0001-5321-5396
dc.contributor.author	Pietroni, N https://orcid.org/0000-0002-8271-2102
dc.contributor.author	Vidal-Calleja, T
dc.date.accessioned	2020-10-21T16:25:28Z
dc.date.available	2020-10-21T16:25:28Z
dc.date.issued	2020-04
dc.identifier.citation	IEEE Robotics and Automation Letters, 2020, 5, (2), pp. 1532-1539
dc.identifier.issn	2377-3766
dc.identifier.issn	2377-3774
dc.identifier.uri	http://hdl.handle.net/10453/143449
dc.description.abstract	© 2016 IEEE. 3D object reconstructions obtained from 2D or 3D cameras are typically noisy. Probabilistic algorithms are suitable for information fusion and can deal with noise robustly. Consequently, these algorithms can be useful for accurate surface reconstruction. This paper presents an approach to estimate a probabilistic representation of the implicit surface of 3D objects. One of the contributions of the paper is the pipeline for generating an accurate reconstruction, given a set of sparse points that are close to the surface and a dense noisy point cloud. A novel submapping method following the topology of the object is proposed to generate conditional independent Gaussian Process Implicit Surfaces. This allows inference and fusion mechanisms to be performed in parallel followed by information propagation through the submaps. Large datasets can efficiently be processed by the proposed pipeline producing not only a surface but also the uncertainty information of the reconstruction. We evaluate the performance of our algorithm using simulated and real datasets.
dc.language	English
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	Meat and Livestock Australia Limited RDCRnD4Profit - 15 - 02 - 031
dc.relation.ispartof	IEEE Robotics and Automation Letters
dc.relation.isbasedon	10.1109/lra.2020.2969175
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject	0913 Mechanical Engineering
dc.title	Skeleton-Based Conditionally Independent Gaussian Process Implicit Surfaces for Fusion in Sparse to Dense 3D Reconstruction
dc.type	Journal Article
utslib.citation.volume	5
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
utslib.copyright.embargo	2022-10-31T00:00:00+1000Z
dc.date.updated	2020-10-21T16:24:57Z
pubs.issue	2
pubs.publication-status	Accepted
pubs.volume	5
utslib.citation.issue	2

Abstract:

© 2016 IEEE. 3D object reconstructions obtained from 2D or 3D cameras are typically noisy. Probabilistic algorithms are suitable for information fusion and can deal with noise robustly. Consequently, these algorithms can be useful for accurate surface reconstruction. This paper presents an approach to estimate a probabilistic representation of the implicit surface of 3D objects. One of the contributions of the paper is the pipeline for generating an accurate reconstruction, given a set of sparse points that are close to the surface and a dense noisy point cloud. A novel submapping method following the topology of the object is proposed to generate conditional independent Gaussian Process Implicit Surfaces. This allows inference and fusion mechanisms to be performed in parallel followed by information propagation through the submaps. Large datasets can efficiently be processed by the proposed pipeline producing not only a surface but also the uncertainty information of the reconstruction. We evaluate the performance of our algorithm using simulated and real datasets.

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http://hdl.handle.net/10453/143449