Abstract The Hough Transform is a well-known method for detecting parameterized objects.
It is the de facto standard for detecting lines and circles in 2-dimensional data sets. For 3D it …

Computer vision technology is moving more and more towards a three-dimensional
approach, and plant phenoty** is following this trend. However, despite its potential, the …

This paper presents a new deep learning architecture called PointGrid that is designed for
3D model recognition from unorganized point clouds. The new architecture embeds the …

Recent works in geometric deep learning have introduced neural networks that allow
performing inference tasks on three-dimensional geometric data by defining convolution …

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 …

This paper describes a benchmark for evaluation of 3D mesh segmentation salgorithms. The
benchmark comprises a data set with 4,300 manually generated segmentations for 380 …

This paper presents a data-driven approach to simultaneous segmentation and labeling of
parts in 3D meshes. An objective function is formulated as a Conditional Random Field …

While computer-aided design is a major part of many modern manufacturing pipelines, the
design files typically generated describe raw geometry. Lost in this representation is the …

We present a review of the state of the art of segmentation and partitioning techniques of
boundary meshes. Recently, these have become a part of many mesh and object …

Mesh partitioning and skeletonisation are fundamental for many computer graphics and
animation techniques. Because of the close link between an object's skeleton and its …