2022
Neri, Paolo; Aruanno, Beatrice; Barone, Sandro; Paoli, Alessandro; Razionale, Armando V.
Semi-automatic point clouds registration for upper limb anatomy Conference
Advances on Mechanics, Design Engineering and Manufacturing IV, 2022, ISBN: 978-3-031-15927-5.
Abstract | Links | BibTeX | Tags: Neural Network., Semi-automatic registration, Upper limb 3D scan
@conference{ 11568_1162043,
title = {Semi-automatic point clouds registration for upper limb anatomy},
author = {Paolo Neri and Beatrice Aruanno and Sandro Barone and Alessandro Paoli and Armando V. Razionale},
doi = {10.1007/978-3-031-15928-2_64},
isbn = {978-3-031-15927-5},
year = {2022},
date = {2022-01-01},
booktitle = {Advances on Mechanics, Design Engineering and Manufacturing IV},
pages = {733–742},
abstract = {In this paper, a semi-automatic procedure to perform point clouds reg-istration is presented. The method was developed for upper limb 3D scanning. During the acquisition, several frames are acquired from different points of view, to obtain a full 360° acquisition of the arm. Each frame stores both the point clouds coordinates and the corresponding RGB image. During post-processing, the RGB image is elaborated through a neural network, to detect relevant key points of the hand, which are then projected to the point clouds. The correspond-ing key points detected from different acquisitions are then used to automatically obtain a rough 3D rotation that aligns the point clouds corresponding to different perspectives in a common reference frame. Finally, the registration is refined through an iterative closest point algorithm. The method was tested on actual arm acquisitions, and the registration results are compared with the conventional fully manual 3-2-1 registration procedure, showing promising results of the proposed method.},
keywords = {Neural Network., Semi-automatic registration, Upper limb 3D scan},
pubstate = {published},
tppubtype = {conference}
}
In this paper, a semi-automatic procedure to perform point clouds reg-istration is presented. The method was developed for upper limb 3D scanning. During the acquisition, several frames are acquired from different points of view, to obtain a full 360° acquisition of the arm. Each frame stores both the point clouds coordinates and the corresponding RGB image. During post-processing, the RGB image is elaborated through a neural network, to detect relevant key points of the hand, which are then projected to the point clouds. The correspond-ing key points detected from different acquisitions are then used to automatically obtain a rough 3D rotation that aligns the point clouds corresponding to different perspectives in a common reference frame. Finally, the registration is refined through an iterative closest point algorithm. The method was tested on actual arm acquisitions, and the registration results are compared with the conventional fully manual 3-2-1 registration procedure, showing promising results of the proposed method.