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Inference-Based Surface Reconstruction of Cluttered Environments

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2 Author(s)
Biggers, K. ; Dept. of Comput. Sci. & Eng., Texas A&M Univ., College Station, TX, USA ; Keyser, J.

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.

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Visualization and Computer Graphics, IEEE Transactions on  (Volume:18 ,  Issue: 8 )