Non-Rigid 3D Point Set Registration With Reliable Hybrid Mixture Model

Haocheng Huang1; Zhengyan Zhang1; Zhe Min2; Max Q.-H. Meng3; Shuang Song1; Jiaole Wang1

10.1109/ROBIO58561.2023.10354537

2023

979-8-3503-2571-3

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)

1-6

4-9 Dec. 2023

Koh Samui, Thailand

Non-rigid point set registration is an essential technique in fields such as robotics, computer vision, image-guided surgery, and augmented reality. However, effectively addressing non-rigid deformations and handling noise interference remains a significant challenge in this context. This paper introduces a novel hybrid mixture model (HMM), combining a Gaussian mixture model (GMM) to describe positional features and a Fisher mixture model (FMM) to represent orientation features. This hybrid mixture model offers a solution to the non-rigid point set registration (PSR) problem. The proposed approach iteratively optimizes model parameters through the maximum expectation (EM) algorithm. It comprehensively explores registration performance, accounting for non-rigid deformations amidst isotropic and anisotropic positional noise. The proposed approach further incorporates reliable normal vectors for evaluating orientation features. Extensive experiments on non-rigid PSR demonstrates the improvements our algorithm offers in terms of both robustness and accuracy.

Nonrigid point set registration partial hybrid mixture model surgical navigation

其他

EI

20240315404350

IEEE

1.School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China
2.School of Control Science and Engineering, Shandong University, Jinan, China
3.Department of Electronic and Electrical Engineering, Southern University of Science and Technology in Shenzhen, China

Haocheng Huang,Zhengyan Zhang,Zhe Min,et al. Non-Rigid 3D Point Set Registration With Reliable Hybrid Mixture Model[C],2023:1-6.