Poisson Surface Reconstruction with Envelope Constraints
Reconstructing surfaces from scanned 3D points has been an important research area for several decades. One common approach that has proven efficient and robust to noise is implicit surface reconstruction, i.e. fitting to the points a 3D scalar function (such as an indicator function or signed‐dista...
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| Veröffentlicht in: | Computer graphics forum Jg. 39; H. 5; S. 173 - 182 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Blackwell Publishing Ltd
01.08.2020
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Reconstructing surfaces from scanned 3D points has been an important research area for several decades. One common approach that has proven efficient and robust to noise is implicit surface reconstruction, i.e. fitting to the points a 3D scalar function (such as an indicator function or signed‐distance field) and then extracting an isosurface. Though many techniques fall within this category, existing methods either impose no boundary constraints or impose Dirichlet/Neumann conditions on the surface of a bounding box containing the scanned data.
In this work, we demonstrate the benefit of supporting Dirichlet constraints on a general boundary. To this end, we adapt the Screened Poisson Reconstruction algorithm to input a constraint envelope in addition to the oriented point cloud. We impose Dirichlet boundary conditions, forcing the reconstructed implicit function to be zero outside this constraint surface. Using a visual hull and/or depth hull derived from RGB‐D scans to define the constraint envelope, we obtain substantially improved surface reconstructions in regions of missing data. |
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| AbstractList | Reconstructing surfaces from scanned 3D points has been an important research area for several decades. One common approach that has proven efficient and robust to noise is implicit surface reconstruction, i.e. fitting to the points a 3D scalar function (such as an indicator function or signed‐distance field) and then extracting an isosurface. Though many techniques fall within this category, existing methods either impose no boundary constraints or impose Dirichlet/Neumann conditions on the surface of a bounding box containing the scanned data.In this work, we demonstrate the benefit of supporting Dirichlet constraints on a general boundary. To this end, we adapt the Screened Poisson Reconstruction algorithm to input a constraint envelope in addition to the oriented point cloud. We impose Dirichlet boundary conditions, forcing the reconstructed implicit function to be zero outside this constraint surface. Using a visual hull and/or depth hull derived from RGB‐D scans to define the constraint envelope, we obtain substantially improved surface reconstructions in regions of missing data. Reconstructing surfaces from scanned 3D points has been an important research area for several decades. One common approach that has proven efficient and robust to noise is implicit surface reconstruction, i.e. fitting to the points a 3D scalar function (such as an indicator function or signed‐distance field) and then extracting an isosurface. Though many techniques fall within this category, existing methods either impose no boundary constraints or impose Dirichlet/Neumann conditions on the surface of a bounding box containing the scanned data. In this work, we demonstrate the benefit of supporting Dirichlet constraints on a general boundary. To this end, we adapt the Screened Poisson Reconstruction algorithm to input a constraint envelope in addition to the oriented point cloud. We impose Dirichlet boundary conditions, forcing the reconstructed implicit function to be zero outside this constraint surface. Using a visual hull and/or depth hull derived from RGB‐D scans to define the constraint envelope, we obtain substantially improved surface reconstructions in regions of missing data. |
| Author | Rusinkiewicz, Szymon Hoppe, Hugues Kazhdan, Misha Chuang, Ming |
| Author_xml | – sequence: 1 givenname: Misha surname: Kazhdan fullname: Kazhdan, Misha email: misha@cs.jhu.edu organization: Johns Hopkins University – sequence: 2 givenname: Ming surname: Chuang fullname: Chuang, Ming email: smr@princeton.edu organization: Johns Hopkins University – sequence: 3 givenname: Szymon surname: Rusinkiewicz fullname: Rusinkiewicz, Szymon email: mingchuang82@gmail.com organization: Princeton University – sequence: 4 givenname: Hugues surname: Hoppe fullname: Hoppe, Hugues email: hhoppe@gmail.com organization: Google |
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| SubjectTerms | Algorithms and systems Boundary conditions Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Geometric algorithms Dirichlet problem languages Missing data Reconstruction |
| Title | Poisson Surface Reconstruction with Envelope Constraints |
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