FasTFit: A fast T-spline fitting algorithm. (November 2017)

• Record Type: Journal Article
• Authors: Feng, Chen; Taguchi, Yuichi
• Is Part Of: Computer aided design. Volume 92(2017)
• Page Start: 11
• Page End: 21
• Publication Date: 2017-11
• Subjects: T-spline -- Point clouds -- Surface fitting -- Bézier patch; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Periodicals; Computer graphics -- Periodicals; Conception technique -- Informatique -- Périodiques; Infographie -- Périodiques; Computer graphics; Engineering design -- Data processing; Periodicals; Electronic journals; 620.00420285
• DOI: 10.1016/j.cad.2017.07.002
• Languages: English
• ISSNs: 0010-4485

Abstract

Abstract: T-spline has been recently developed to represent objects of arbitrary shapes using a smaller number of control points than the conventional NURBS or B-spline representations in computer aided design, computer graphics, and reverse engineering. However, existing methods for fitting a T-spline over a point cloud are slow. By shifting away from the conventional iterative fit-and-refine paradigm, we present a novel split-connect-fit algorithm to more efficiently perform the T-spline fitting. Through adaptively dividing a point cloud into a set of B-spline patches, we first discover a proper topology of T-spline control points, i.e., the T-mesh. We then connect these B-spline patches into a single T-spline surface with different continuity options between neighboring patches according to the data. The T-spline control points are initialized from their correspondences in the B-spline patches, which are refined by using a conjugate gradient method. In experiments using several types of large-sized point clouds, we demonstrate that our algorithm is at least an order of magnitude faster than state-of-the-art algorithms while provides comparable or better results in terms of quality and conciseness. Highlights: We propose FasTFit, a fast algorithm to fit T-splines on point clouds. It is at least 10x faster than state-of-the-art methods with comparable results. Its single frame surface reconstruction results are comparable to KinectFusion. It is applicable to point clouds, digital terrain models, and images. Graphical abstract: