Authors:
Christiano Couto Gava
;
Alain Pagani
;
Bernd Krolla
and
Didier Stricker
Affiliation:
German Research Center for Artificial Intelligence, Germany
Keyword(s):
Robust Guided Matching, Feature Detection, Spherical Imaging, 3D Reconstruction, Multi-view Stereo.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Applications and Services
;
Camera Networks and Vision
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Software Engineering
;
Stereo Vision and Structure from Motion
Abstract:
We present a novel, robust guided matching technique. Given a set of calibrated spherical images along with
the associated sparse 3D point cloud, our approach consistently finds matches across the images in a multilayer
feature detection framework. New feature matches are used to refine existing 3D points or to add reliable
ones to the point cloud, therefore improving scene representation. We use real indoor and outdoor scenarios
to validate the robustness of the proposed approach. Moreover, we perform a quantitative evaluation of our
technique to demonstrate its effectiveness.