We present an inference-based surface reconstruction algorithm that is capable of identifying objects of interest among a cluttered scene, and reconstructing solid model representations even in the presence of occluded surfaces. Our proposed approach incorporates a predictive modeling framework that uses a set of user-provided models for prior knowledge, and applies this knowledge to the iterative identification and construction process. Our approach uses a local to global construction process guided by rules for fitting high-quality surface patches obtained from these prior models. We demonstrate the application of this algorithm on several example data sets containing heavy clutter and occlusion. © 2012 IEEE.
|Original language||English (US)|
|Number of pages||13|
|Journal||IEEE Transactions on Visualization and Computer Graphics|
|State||Published - Aug 2012|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This work was supported in part by US National ScienceFoundation (NSF) Grant IIS-0917286 and by AwardNo. KUS-C1-016-04 from King Abdullah University ofScience and Technology. The authors would like to thankAnn McNamara for the use of her scanner and laboratory,and the Stanford 3D Scanning Repository for the Bunnymodel used in our figures.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.