Volume-Aware Surface Evolution for Surface Reconstruction from Incomplete Point Clouds


Andrea Tagliasacchi1, Matt Olson1, Hao Zhang1, Ghassan Hamarneh1, Daniel Cohen-Or2

1School of Computing Science, Simon Fraser University, Canada
2School of Computing Science, Tel Aviv University, Israel

Objects with many concavities are difficult to acquire using laser scanners. The highly concave areas are hard to access by a scanner due to occlusions by other components of the object. The resulting point scan typically suffers from large amounts of missing data. Methods that use surface-based priors rely on local surface estimates and perform well only when filling small holes. When the holes become large, the reconstruction problem becomes severely under-constrained, which necessitates the use of additional reconstruction priors. In this paper, we introduce weak volumetric priors which assume that the volume of a shape varies smoothly and that each point cloud sample is visible from outside the shape. Specifically, the union of view-rays given by the scanner implicitly carves the exterior volume, while volumetric smoothness regularizes the internal volume. We incorporate these priors into a surface evolution framework where a new energy term defined by volumetric smoothness is introduced to handle large amount of missing data. We demonstrate the effectiveness of our method on objects exhibiting deep concavities, and show its general applicability over a broader spectrum of geometric scenario.

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We would like to thank Paolo Cignoni for the useful discussions and his help while working with the Meshlab API and Ehsan Shokrgozar for his help modeling the synthetic shape in the teaser. This work was supported by the SFU Graduate Fellowship program, the Natural Sciences and Engineering Research Council of Canada (NSERC grant #611370), the Israeli Ministry of Science and the Israel Science Foundation.