Incremental 3D reconstruction using Bayesian learning

Authors:
Ze-Huan Yuan;Tong Lu
Affiliations:
State Key Laboratory of Software Novel Technology, Nanjing University, Nanjing, China 210023;State Key Laboratory of Software Novel Technology, Nanjing University, Nanjing, China 210023 and Jiangyin Institute of Information Technology of Nanjing University, Nanjing, China 210023
Venue:
Applied Intelligence
Year:
2013

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Abstract

We present a novel algorithm for 3D reconstruction in this paper, converting incremental 3D reconstruction to an optimization problem by combining two feature-enhancing geometric priors and one photometric consistency constraint under the Bayesian learning framework. Our method first reconstructs an initial 3D model by selecting uniformly distributed key images using a view sphere. Then once a new image is added, we search its correlated reconstructed patches and incrementally update the result model by optimizing the geometric and photometric energy terms. The experimental results illustrate our method is effective for incremental 3D reconstruction and can be further applied for large-scale datasets or to real-time reconstruction.