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Title: An image processing approach to feature-preserving B-spline surface fairing
Authors: Kawasaki, Taro
Jayaraman, Pradeep Kumar
Shida, Kentaro
Zheng, Jianmin
Maekawa, Takashi
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Kawasaki, T., Jayaraman, P. K., Shida, K., Zheng, J., & Maekawa, T. (2018). An image processing approach to feature-preserving B-spline surface fairing. Computer-Aided Design, 99, 1-10. doi:10.1016/j.cad.2018.01.003
Journal: Computer-Aided Design
Abstract: Reverse engineering of 3D industrial objects such as automobiles and electric appliances is typically performed by fitting B-spline surfaces to scanned point cloud data with a fairing term to ensure smoothness, which often smooths out sharp features. This paper proposes a radically different approach to constructing fair B-spline surfaces, which consists of fitting a surface without a fairing term to capture sharp edges, smoothing the normal field of the constructed surface with feature preservation, and reconstructing the B-spline surface from the smoothed normal field. The core of our method is an image processing based feature-preserving normal field fairing technique. This is inspired by the success of many recent research works on the use of normal field for reconstructing mesh models, and makes use of the impressive simplicity and effectiveness of bilateral-like filtering for image denoising. In particular, our approach adaptively partitions the B-spline surface into a set of segments such that each segment has approximately uniform parameterization, generates an image from each segment in the parameter space whose pixel values are the normal vectors of the surface, and then applies a bilateral filter in the parameter domain to fair the normal field. As a result, our approach inherits the advantages of image bilateral filtering techniques and is able to effectively smooth B-spline surfaces with feature preservation as demonstrated by various examples.
ISSN: 0010-4485
DOI: 10.1016/j.cad.2018.01.003
Rights: © 2018 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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