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|a 10.1109/TPAMI.2022.3178969
|2 doi
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|a eng
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| 100 |
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|a Bignardi, Samuel
|e verfasserin
|4 aut
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| 245 |
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|a Radar-Based Shape and Reflectivity Reconstruction Using Active Surfaces and the Level Set Method
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| 264 |
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 07.04.2023
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|a Date Revised 11.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a We investigate a multiview shape reconstruction problem based on an active surface model whose geometric evolution is driven by radar measurements acquired at sparse locations. Building on our previous work in the context of variational methods for the reconstruction of a scene conceptualized as the graph of a function, we generalize this inversion approach for a general geometry, now described by an active surface, strongly motivated by prior variational computer vision approaches to multiview stereo reconstruction from camera images. While conceptually similar, use of radar echoes within a variational scheme to drive the active surface evolution requires significant changes in regularization strategies compared to prior image based methodologies for the active surface evolution to work effectively. We describe all of these aspects and how we addressed them. While our long term objective is to develop a framework capable of fusing radar as well as other image based information, in which the active surface becomes an explicit shared reference for data fusion. In this paper, we explore the reconstruction using radar as a single modality, demonstrating that the presented approach can provide reconstructions of quality comparable to those from image based methods showing great potential for further development toward data fusion
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|a Journal Article
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1 |
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|a Sandhu, Romeil
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yezzi, Anthony
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 3 vom: 01. März, Seite 3617-3631
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:45
|g year:2023
|g number:3
|g day:01
|g month:03
|g pages:3617-3631
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|u http://dx.doi.org/10.1109/TPAMI.2022.3178969
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