Add to ORKGWe consider applications of clustering techniques, Mean Shift and Self-Organizing Maps, to surface reconstruction (meshing) from scattered point data and review a novel kernel-based clustering method
In this paper, we develop a method for robust filtering of a noisy set of points sampled from a smoo...
The area of surface reconstruction has seen substantial progress in the past two decades. The tradit...
We describe a new method for surface reconstruction and smoothing based on unorganized noisy point c...
We consider applications of clustering techniques, Mean Shift and Self-Organizing Maps, to surface r...
We present an automatic method for the generation of surface triangulations from sets of scattered p...
3D modelling finds a wide range of applications in robot vision and reverse engineering. However due...
Understanding when a cloud of points in three-dimensional space can be, semantically, interpreted as...
Clustering data is a standard tool to reduce large data sets, such as scans from a LiDAR, enabling r...
In this paper, a novel approach to face clustering is proposed. The aim is the completely unsupervis...
The paper delivers a brief overview of recent developments in the field of surface reconstruction fr...
We survey and benchmark traditional and novel learning-based algorithms that address the problem of ...
We present a novel surface model and reconstruction method for man-made environments that take prior...
International audienceIn this paper we propose a framework for piecewise mesh-based 3D reconstructio...
This paper proposes a method in which the body surface is reconstructed from a point cloud (set of p...
Today, polygonal models occur everywhere in graphical applications, since they are easy to render a...
In this paper, we develop a method for robust filtering of a noisy set of points sampled from a smoo...
The area of surface reconstruction has seen substantial progress in the past two decades. The tradit...
We describe a new method for surface reconstruction and smoothing based on unorganized noisy point c...
We consider applications of clustering techniques, Mean Shift and Self-Organizing Maps, to surface r...
We present an automatic method for the generation of surface triangulations from sets of scattered p...
3D modelling finds a wide range of applications in robot vision and reverse engineering. However due...
Understanding when a cloud of points in three-dimensional space can be, semantically, interpreted as...
Clustering data is a standard tool to reduce large data sets, such as scans from a LiDAR, enabling r...
In this paper, a novel approach to face clustering is proposed. The aim is the completely unsupervis...
The paper delivers a brief overview of recent developments in the field of surface reconstruction fr...
We survey and benchmark traditional and novel learning-based algorithms that address the problem of ...
We present a novel surface model and reconstruction method for man-made environments that take prior...
International audienceIn this paper we propose a framework for piecewise mesh-based 3D reconstructio...
This paper proposes a method in which the body surface is reconstructed from a point cloud (set of p...
Today, polygonal models occur everywhere in graphical applications, since they are easy to render a...
In this paper, we develop a method for robust filtering of a noisy set of points sampled from a smoo...
The area of surface reconstruction has seen substantial progress in the past two decades. The tradit...
We describe a new method for surface reconstruction and smoothing based on unorganized noisy point c...