Bayesian mesh reconstruction from noisy point data

Authors:
Guiping Qian;Ruofeng Tong;Wen Peng;Jinxing Dong
Affiliations:
State Key Laboratory of CADSCG, Zhejiang University, Hangzhou, China;State Key Laboratory of CADSCG, Zhejiang University, Hangzhou, China;State Key Laboratory of CADSCG, Zhejiang University, Hangzhou, China;State Key Laboratory of CADSCG, Zhejiang University, Hangzhou, China
Venue:
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
Year:
2006

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Abstract

This paper presents a new reverse engineering method for creating 3D mesh models, which approximates unorganized noisy point data without orientation information. The main idea of the method is based on statistics Bayesian model. Firstly the feature enhancing prior probabilities over 3D data retrieve the sharp features, such as edges or corners. Then a local polynomial probabilistic model is used to approximate a continuous differentiable manifold. The Bayesian model uses an iterative fitting clustering algorithm to improve the noise tolerance in geometry accuracy. After iteration a density-based estimation function can automatically remove the outliers. Furthermore, the current sphere cover meshing approach is improved to reconstruct the mesh surface from the noisy point data. Experimental results indicate that our approach is robust and efficient. It can be well applied to smoothing noisy data, removing outliers, enhancing features and mesh reconstruction.