The Community for Technology Leaders
Green Image
Issue No. 08 - Aug. (2012 vol. 18)
ISSN: 1077-2626
pp: 1255-1267
K. Biggers , Dept. of Comput. Sci. & Eng., Texas A&M Univ., College Station, TX, USA
J. Keyser , Dept. of Comput. Sci. & Eng., Texas A&M Univ., College Station, TX, USA
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.
surface reconstruction, hidden feature removal, solid modelling, construction process, surface reconstruction, cluttered environments, inference based surface reconstruction, solid model representations, occluded surfaces, predictive modeling, user provided models, iterative identification, Surface reconstruction, Object recognition, Solid modeling, Shape, Surface treatment, Solids, Computational modeling, surface fitting., Three-dimensional/stereo scene analysis, object recognition, segmentation

K. Biggers and J. Keyser, "Inference-Based Surface Reconstruction of Cluttered Environments," in IEEE Transactions on Visualization & Computer Graphics, vol. 18, no. , pp. 1255-1267, 2012.
95 ms
(Ver 3.3 (11022016))