Authors:
Jens Garstka
and
Gabriele Peters
Affiliation:
University of Hagen, Germany
Keyword(s):
3-D Keypoint Detection, 3-D Recognition, 3-D Computer Vision.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Perception and Awareness
;
Robotics and Automation
;
Virtual Environment, Virtual and Augmented Reality
;
Vision, Recognition and Reconstruction
Abstract:
In robot perception, as well as in other areas of 3-D computer vision, keypoint detection is the first major
step for an efficient and accurate 3-D perception of the environment. Thus, a fast and robust algorithm for an
automatic identification of keypoints in unstructured 3-D point clouds is essential. The presented algorithm is
designed to be highly parallelizable and can be implemented on modern GPUs for fast execution. The computation
is based on a convolution of a voxel based representation of the point cloud and a voxelized integral
volume. The generation of the voxel-based representation neither requires additional surface information or
normals nor needs to approximate them. The proposed approach is robust against noise up to the mean distance
between the 3-D points. In addition, the algorithm provides moderate scale invariance, i. e., it can approximate
keypoints for lower resolution versions of the input point cloud. This is particularly useful, if keypoints are
supposed to
be used with any local 3-D point cloud descriptor to recognize or classify point clouds at different
scales. We evaluate our approach in a direct comparison with state-of-the-art keypoint detection algorithms in
terms of repeatability and computation time.
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