TemPCC: Completing Temporal Occlusions in Large Dynamic Point Clouds captured by Multiple RGB-D Cameras

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dc.contributor.authorMühlenbrock, Andreen_US
dc.contributor.authorWeller, Reneen_US
dc.contributor.authorZachmann, Gabrielen_US
dc.contributor.editorCeylan, Duyguen_US
dc.contributor.editorLi, Tzu-Maoen_US
dc.date.accessioned2025-05-09T09:35:42Z
dc.date.available2025-05-09T09:35:42Z
dc.date.issued2025
dc.description.abstractWe present TemPCC, an approach to complete temporal occlusions in large dynamic point clouds. Our method manages a point set over time, integrates new observations into this set, and predicts the motion of occluded points based on the flow of surrounding visible ones. Unlike existing methods, our approach efficiently handles arbitrarily large point sets with linear complexity, does not reconstruct a canonical representation, and considers only local features. Our tests, performed on an Nvidia GeForce RTX 4090, demonstrate that our approach can complete a frame with 30,000 points in under 30 ms, while, in general, being able to handle point sets exceeding 1,000,000 points. This scalability enables the mitigation of temporal occlusions across entire scenes captured by multi-RGB-D camera setups. Our initial results demonstrate that self-occlusions are effectively completed and successfully generalized to unknown scenes despite limited training data.en_US
dc.description.sectionheadersShort Paper 3
dc.description.seriesinformationEurographics 2025 - Short Papers
dc.identifier.doi10.2312/egs.20251039
dc.identifier.isbn978-3-03868-268-4
dc.identifier.issn1017-4656
dc.identifier.pages4 pages
dc.identifier.urihttps://doi.org/10.2312/egs.20251039
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/egs20251039
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 → Point-based models; Information systems → Spatial-temporal systems
dc.subjectComputing methodologies → Point
dc.subjectbased models
dc.subjectInformation systems → Spatial
dc.subjecttemporal systems
dc.titleTemPCC: Completing Temporal Occlusions in Large Dynamic Point Clouds captured by Multiple RGB-D Camerasen_US
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