Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/160092
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dc.contributor.authorXiaoshui Huangen_US
dc.contributor.authorJian Zhangen_US
dc.contributor.authorLixin Fanen_US
dc.contributor.authorQiang Wuen_US
dc.contributor.authorChun Yuanen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:20:15Z-
dc.date.available2020-04-06T07:20:15Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TIP.2017.2695888en_US
dc.identifier.urihttp://localhost/handle/Hannan/160092-
dc.description.abstractWe propose a systematic approach for registering cross-source point clouds that come from different kinds of sensors. This task is especially challenging due to the presence of significant missing data, large variations in point density, scale difference, large proportion of noise, and outliers. The robustness of the method is attributed to the extraction of macro and micro structures. Macro structure is the overall structure that maintains similar geometric layout in cross-source point clouds. Micro structure is the element (e.g., local segment) being used to build the macro structure. We use graph to organize these structures and convert the registration into graph matching. With a novel proposed descriptor, we conduct the graph matching in a discriminative feature space. The graph matching problem is solved by an improved graph matching solution, which considers global geometrical constraints. Robust cross source registration results are obtained by incorporating graph matching outcome with RANSAC and ICP refinements. Compared with eight state-of-the-art registration algorithms, the proposed method invariably outperforms on Pisa Cathedral and other challenging cases. In order to compare quantitatively, we propose two challenging cross-source data sets and conduct comparative experiments on more than 27 cases, and the results show we obtain much better performance than other methods. The proposed method also shows high accuracy in same-source data sets.en_US
dc.format.extent3261,en_US
dc.format.extent3276en_US
dc.publisherIEEEen_US
dc.relation.haspart7904634.pdfen_US
dc.titleA Systematic Approach for Cross-Source Point Cloud Registration by Preserving Macro and Micro Structuresen_US
dc.typeArticleen_US
dc.journal.volume26en_US
dc.journal.issue7en_US
Appears in Collections:2017

Files in This Item:
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7904634.pdf5.56 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorXiaoshui Huangen_US
dc.contributor.authorJian Zhangen_US
dc.contributor.authorLixin Fanen_US
dc.contributor.authorQiang Wuen_US
dc.contributor.authorChun Yuanen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:20:15Z-
dc.date.available2020-04-06T07:20:15Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TIP.2017.2695888en_US
dc.identifier.urihttp://localhost/handle/Hannan/160092-
dc.description.abstractWe propose a systematic approach for registering cross-source point clouds that come from different kinds of sensors. This task is especially challenging due to the presence of significant missing data, large variations in point density, scale difference, large proportion of noise, and outliers. The robustness of the method is attributed to the extraction of macro and micro structures. Macro structure is the overall structure that maintains similar geometric layout in cross-source point clouds. Micro structure is the element (e.g., local segment) being used to build the macro structure. We use graph to organize these structures and convert the registration into graph matching. With a novel proposed descriptor, we conduct the graph matching in a discriminative feature space. The graph matching problem is solved by an improved graph matching solution, which considers global geometrical constraints. Robust cross source registration results are obtained by incorporating graph matching outcome with RANSAC and ICP refinements. Compared with eight state-of-the-art registration algorithms, the proposed method invariably outperforms on Pisa Cathedral and other challenging cases. In order to compare quantitatively, we propose two challenging cross-source data sets and conduct comparative experiments on more than 27 cases, and the results show we obtain much better performance than other methods. The proposed method also shows high accuracy in same-source data sets.en_US
dc.format.extent3261,en_US
dc.format.extent3276en_US
dc.publisherIEEEen_US
dc.relation.haspart7904634.pdfen_US
dc.titleA Systematic Approach for Cross-Source Point Cloud Registration by Preserving Macro and Micro Structuresen_US
dc.typeArticleen_US
dc.journal.volume26en_US
dc.journal.issue7en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7904634.pdf5.56 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorXiaoshui Huangen_US
dc.contributor.authorJian Zhangen_US
dc.contributor.authorLixin Fanen_US
dc.contributor.authorQiang Wuen_US
dc.contributor.authorChun Yuanen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:20:15Z-
dc.date.available2020-04-06T07:20:15Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TIP.2017.2695888en_US
dc.identifier.urihttp://localhost/handle/Hannan/160092-
dc.description.abstractWe propose a systematic approach for registering cross-source point clouds that come from different kinds of sensors. This task is especially challenging due to the presence of significant missing data, large variations in point density, scale difference, large proportion of noise, and outliers. The robustness of the method is attributed to the extraction of macro and micro structures. Macro structure is the overall structure that maintains similar geometric layout in cross-source point clouds. Micro structure is the element (e.g., local segment) being used to build the macro structure. We use graph to organize these structures and convert the registration into graph matching. With a novel proposed descriptor, we conduct the graph matching in a discriminative feature space. The graph matching problem is solved by an improved graph matching solution, which considers global geometrical constraints. Robust cross source registration results are obtained by incorporating graph matching outcome with RANSAC and ICP refinements. Compared with eight state-of-the-art registration algorithms, the proposed method invariably outperforms on Pisa Cathedral and other challenging cases. In order to compare quantitatively, we propose two challenging cross-source data sets and conduct comparative experiments on more than 27 cases, and the results show we obtain much better performance than other methods. The proposed method also shows high accuracy in same-source data sets.en_US
dc.format.extent3261,en_US
dc.format.extent3276en_US
dc.publisherIEEEen_US
dc.relation.haspart7904634.pdfen_US
dc.titleA Systematic Approach for Cross-Source Point Cloud Registration by Preserving Macro and Micro Structuresen_US
dc.typeArticleen_US
dc.journal.volume26en_US
dc.journal.issue7en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7904634.pdf5.56 MBAdobe PDF