Dynamic 3D Mesh Reconstruction Based on Nonrigid Iterative Closest-Farthest Points Registration

Abstract

Fitting apparel and apparel in performing different activities is essential for the functional yet comfortable experience of the user. 4D scans, i.e. 3D scans in continuous timestamps, of the body (part) in performing those activities are the basis for the design of garments/apparel in 4D. In this paper, we proposed a semi-automatic workflow for constructing 4D scans of the body parts with the emphasis on registering noisy scans at a given timestamp. Continuous 3D scans regarding the moving body parts are captured first from different depth cameras from different view angles. In a given timestamp, the collected 3D scans are roughly aligned to a template using the rigid Iterative Closest Points (ICP) algorithm. Then these scans are further registered using a newly proposed non-rigid Iterative Closest-Farthest Points (ICFP) algorithm, in which correspondences between the source and the target are established by either closest or farthest points based on the newly defined logical distance concept and the probability theory. Experimental results indicated that the ICFP method is robust against noise and the scanning accuracy can be as high as 3.4 %. It also reveals that, for the human foot, the differences of ball width and ball angles between the loaded and the unloaded situation can be as large as 8 mm and 2 degrees, respectively. This highlights the importance of using 4D scan in designing garments and apparel.

Optimal Position of Cameras Design in a 4D Foot Scanner

“…According to the method each cleaned data-set is rigidly registered on a reference model to build a rough point-cloud of the foot, then the reference model is non-rigidly registered on the point-cloud to have a meaningful mesh. Due to the larger overlap between each data-set captured by each camera in the proposed method than the study in [40], their registration method shows better performance though our camera arrangement comparing to their non- optimal camera arrangement. Accordingly, the used reference model (source mesh) is shown in FIGURE 7(a).…”

“…Through the explained 3D registration method in [40] which uses a probability function [41][42][43][44], we use seven cleaned frames captured by each camera to reconstruct the foot. According to the method each cleaned data-set is rigidly registered on a reference model to build a rough point-cloud of the foot, then the reference model is non-rigidly registered on the point-cloud to have a meaningful mesh.…”