Viewpoint-independent object recognition using reduced-dimension point cloud data
Abstract
Point cloud data offer the potential for viewpoint-independent object recognition based solely on the geometrical information about an object that they contain. We consider two types of one-dimensional data products extracted from point clouds: range histograms and point-separation histograms. We evaluate each histogram in terms of its viewpoint independence. The Jensen-Shannon divergence is used to show that point-separation histograms have the potential for viewpoint independence. We demonstrate viewpoint-independent recognition performance using lidar data sets from two vehicles and a simple algorithm for a two-class recognition problem. We find that point-separation histograms have good potential for viewpoint-independent recognition over a hemisphere.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 26, 2021
- Source ID
- 10.1364/josaa.427957
Entities
People
- Edward A. Watson
Organizations
- Air Force Research Laboratory
- University of Dayton