Point Set Denoising using a Variational Bayesian Method

Yoon, Mincheol; Ivrissimtzis, Ioannis

Abstract

For statistical modeling, the model parameters are usually estimated by maximizing a probability measure, such as the likelihood or the posterior. In contrast, a variational Bayesian method threats the parameters of the model as probability distributions and computes optimal distributions for them rather than values. It has been shown that this approach effectively avoids the overfitting problem, which is common with other parameter optimization methods. This paper applies a variational Bayesian technique to surface fitting for height field data. Then, we propose point cloud denoising based on the basic surface fitting technique. Validation experiments and further tests with scan data verify the robustness of the proposed method.

Citation

Yoon, M., & Ivrissimtzis, I. (2008). Point Set Denoising using a Variational Bayesian Method. Jeongbo gwahaghoe nonmunji. keompyuting ui silje, 14(5), 527-531

• Journal Article Type: Article
• Publication Date: Jan 1, 2008
• Deposit Date: Sep 28, 2010
• Journal: 정보과학회논문지 : 컴퓨팅의 실제
• Print ISSN: 1229-7712
• Publisher: Korean Institute of Information Scientists and Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 5
• Pages: 527-531
• Keywords: Variational Bayesian method, Point set denoising, Overfitting control, Height field fitting, Computer Science, Artificial Intelligence,Computer Science, Cybernetics,Computer Science, Hardware &architecture,Computer Science, Information Systems,Computer Science, Software Engineering.
• Public URL: https://durham-repository.worktribe.com/output/1539544
• Publisher URL: http://ksci.kisti.re.kr/browse/browResult.ksci?browseBean.issSeq=JBGHIF_2008_v14n5