Adjoint Bijective ZoomOut: Efficient Upsampling for Learned Linearly-invariant Embedding

Simple item page
dc.contributor.authorViganò, Giulioen_US
dc.contributor.authorMelzi, Simoneen_US
dc.contributor.editorBanterle, Francescoen_US
dc.contributor.editorCaggianese, Giuseppeen_US
dc.contributor.editorCapece, Nicolaen_US
dc.contributor.editorErra, Ugoen_US
dc.contributor.editorLupinetti, Katiaen_US
dc.contributor.editorManfredi, Gildaen_US
dc.date.accessioned2023-11-12T15:37:35Z
dc.date.available2023-11-12T15:37:35Z
dc.date.issued2023
dc.description.abstractIn this paper, we present a novel method for refining correspondences between 3D point clouds. Our method is compatible with the functional map framework, so it relies on the spectral representation of the correspondence. Although, differently from other similar approaches, this algorithm is specifically for a particular functional setting, being the only refinement method compatible with a recent data-driven approach, more suitable for point cloud matching. Our algorithm arises from a different way of converting functional operators into point-to-point correspondence, which we prove to promote bijectivity between maps, exploiting a theoretical result. Iterating this procedure and performing spectral upsampling in the same way as other similar methods, ours increases the accuracy of the correspondence, leading to more bijective correspondences. We tested our method over different datasets. It outperforms the previous methods in terms of map accuracy in all the tests considered.en_US
dc.description.sectionheadersGeometry processing
dc.description.seriesinformationSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
dc.identifier.doi10.2312/stag.20231293
dc.identifier.isbn978-3-03868-235-6
dc.identifier.issn2617-4855
dc.identifier.pages37-46
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/stag.20231293
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/stag20231293
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Computer graphics; Machine learning
dc.subjectComputing methodologies
dc.subjectComputer graphics
dc.subjectMachine learning
dc.titleAdjoint Bijective ZoomOut: Efficient Upsampling for Learned Linearly-invariant Embeddingen_US
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
037-046.pdf
Size:
10.93 MB
Format:
Adobe Portable Document Format
Download
Collections
Italian Chapter Conference 2023 - Smart Tools and Apps in Graphics