3D Nonrigid Registration for Partially Deformable Objects and Principal Curvatures Based Ridge Detection

Abstract

This thesis presents a fast 3D nonrigid registration algorithm based on a simplified model to represent partially deformed objects and an efficient algorithm to detect ridges employed via determining principal curvatures of 3D objects Since structured light 3D sensing suffers from quite poor edge reconstruction it usually leads to poor geometry in high-curvature regions To solve this problem we try to detect ridges from a 3D template model and then register both of the template model and its ridges to the real data We adopted principal component analysis (PCA) based method to estimate principal curvatures which greatly can eliminate the effect of noise Next we determined either convex or concave surfaces to distinguish between ridges and valleys and select high curvature region as the region of interest (ROI) To find ridge points the local maximum of curvature on surface the search space defined by principal directions is used to get points with maximum curvature Finally curve fitting is used to generate a smooth and continuous ridge curve to meet our requirement Nonrigid registration is a challenging optimization problem since the solution space includes both alignment and deformation which is much harder than the rigid registration We designed a low degrees of freedom (DoF) model defined by using straight skeleton to represent partial deformation of objects The proposed schemes can efficiently solve the optimization of registration and prevent overfitting Moreover a proper initialization for registration can help derive more reliable correspondences which can avoid stuck in local minimum and help converge faster during optimization We also proposed to initialize a functional model from low-to-high DoF for improving the stability and accuracy of registration We have demonstrated and evaluated results by including objects with various deformation

Date of Award	2020
Original language	English
Supervisor	Ming-Der Shieh (Supervisor)