Abstract—Intense research activity on 3D data analysis tasks, such as object recognition and shape retrieval, has recently fostered the proposal of many techniques to perform detection of repeatable and distinctive keypoints in 3D surfaces. This high number of proposals has not been accompanied yet by a comprehensive comparative evaluation of the methods. Moti-vated by this, our work proposes a performance evaluation of the state-of-the-art in 3D keypoint detection, mainly addressing the task of 3D object recognition. The evaluation is carried out by analyzing the performance of several prominent methods in terms of robustness to noise (real and synthetic), presence of clutter, occlusions and point-of-view variations. Keywords-3D detectors;...
The goal of this work is to evaluate 3D keypoints detectors and descriptors, which could be used for...
Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of object...
This paper presents the first performance evaluation of interest points on scalar volumetric data. S...
Intense research activity on 3D data analysis tasks, such as object recognition and shape retrieval,...
In the past few years detection of repeatable and distinctive keypoints on 3D surfaces has been the ...
Abstract: When processing 3D point cloud data, features must be extracted from a small set of points...
A number of 3D local feature descriptors have been proposed in the literature. It is however, unclea...
© 2017 IEEE. In 3D object recognition, local feature-based recognition is known to be robust against...
This paper presents the first performance evaluation of interest points on scalar volumetric data. S...
none5noThe established approach to 3D keypoint detection consists in defining effective handcrafted ...
Matching surfaces is a challenging 3D Computer Vision problem typically addressed by local features....
The extraction and description of keypoints as salient image parts has a long tradition within proce...
none4noKeypoint detection represents the first stage in the majority of modern computer vision pipel...
International audienceIn this paper, we propose a new 3D object recognition method that employs a se...
Three-dimensional local feature detection and description techniques are widely used for object regi...
The goal of this work is to evaluate 3D keypoints detectors and descriptors, which could be used for...
Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of object...
This paper presents the first performance evaluation of interest points on scalar volumetric data. S...
Intense research activity on 3D data analysis tasks, such as object recognition and shape retrieval,...
In the past few years detection of repeatable and distinctive keypoints on 3D surfaces has been the ...
Abstract: When processing 3D point cloud data, features must be extracted from a small set of points...
A number of 3D local feature descriptors have been proposed in the literature. It is however, unclea...
© 2017 IEEE. In 3D object recognition, local feature-based recognition is known to be robust against...
This paper presents the first performance evaluation of interest points on scalar volumetric data. S...
none5noThe established approach to 3D keypoint detection consists in defining effective handcrafted ...
Matching surfaces is a challenging 3D Computer Vision problem typically addressed by local features....
The extraction and description of keypoints as salient image parts has a long tradition within proce...
none4noKeypoint detection represents the first stage in the majority of modern computer vision pipel...
International audienceIn this paper, we propose a new 3D object recognition method that employs a se...
Three-dimensional local feature detection and description techniques are widely used for object regi...
The goal of this work is to evaluate 3D keypoints detectors and descriptors, which could be used for...
Keypoint detection is a basic step in many computer vision algorithms aimed at recognition of object...
This paper presents the first performance evaluation of interest points on scalar volumetric data. S...