CoupNeRF: Property-aware Neural Radiance Fields for Multi-Material Coupled Scenario Reconstruction

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dc.contributor.authorLi, Jinen_US
dc.contributor.authorGao, Yangen_US
dc.contributor.authorSong, Wenfengen_US
dc.contributor.authorLi, Yacongen_US
dc.contributor.authorLi, Shuaien_US
dc.contributor.authorHao, Aiminen_US
dc.contributor.authorQin, Hongen_US
dc.contributor.editorChen, Renjieen_US
dc.contributor.editorRitschel, Tobiasen_US
dc.contributor.editorWhiting, Emilyen_US
dc.date.accessioned2024-10-13T18:07:13Z
dc.date.available2024-10-13T18:07:13Z
dc.date.issued2024
dc.description.abstractNeural Radiance Fields (NeRFs) have achieved significant recognition for their proficiency in scene reconstruction and rendering by utilizing neural networks to depict intricate volumetric environments. Despite considerable research dedicated to reconstructing physical scenes, rare works succeed in challenging scenarios involving dynamic, multi-material objects. To alleviate, we introduce CoupNeRF, an efficient neural network architecture that is aware of multiple material properties. This architecture combines physically grounded continuum mechanics with NeRF, facilitating the identification of motion systems across a wide range of physical coupling scenarios. We first reconstruct specific-material of objects within 3D physical fields to learn material parameters. Then, we develop a method to model the neighbouring particles, enhancing the learning process specifically in regions where material transitions occur. The effectiveness of CoupNeRF is demonstrated through extensive experiments, showcasing its proficiency in accurately coupling and identifying the behavior of complex physical scenes that span multiple physics domains.en_US
dc.description.number7
dc.description.sectionheaders3D Reconstruction and Novel View Synthesis I
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15208
dc.identifier.issn1467-8659
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15208
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15208
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Computing methodologies → Physical simulation; Rendering; Reconstruction
dc.subjectComputing methodologies → Physical simulation
dc.subjectRendering
dc.subjectReconstruction
dc.titleCoupNeRF: Property-aware Neural Radiance Fields for Multi-Material Coupled Scenario Reconstructionen_US
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