This paper surveys mesh segmentation techniques and algorithms, with a focus on part‐
based segmentation, that is, segmentation that divides a mesh (featuring a 3D object) into …

Large repositories of 3D shapes provide valuable input for data-driven analysis and
modeling tools. They are especially powerful once annotated with semantic information such …

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most
important and challenging types of system in clinical orthopedics, as they enable precise …

In this paper, we present a novel approach for 3D dental model segmentation via deep
Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to …

Segmentation of nasopharyngeal carcinoma (NPC) from computed tomography (CT) image
is conducive to the clinical healthcare. Nevertheless, due to the large shape variations …

This article presents a novel approach for 3D mesh labeling by using deep Convolutional
Neural Networks (CNNs). Many previous methods on 3D mesh labeling achieve impressive …

Data-driven methods serve an increasingly important role in discovering geometric,
structural, and semantic relationships between shapes. In contrast to traditional approaches …

Abstract 3D mesh segmentation has become a crucial part of many applications in 3D shape
analysis. In this paper, a comprehensive survey on 3D mesh segmentation methods is …

Classifying 3D measurement data has become a core problem in photogrammetry and 3D
computer vision, since the rise of modern multiview geometry techniques, combined with …

Abstract Background and Objective: Cough audio signal classification is a potentially useful
tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect …