title: Inference-based surface reconstruction of cluttered environments.
abstract: 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.
authors.affiliation: Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843-3112, USA. biggers@tamu.edu
authors.forename: Keith
authors.initials: K
authors.lastname: Biggers
authors.forename: John
authors.initials: J
authors.lastname: Keyser