Authors:
Ahmad Kamal Aijazi
;
Paul Checchin
and
Laurent Trassoudaine
Affiliation:
Clermont Université, France
Keyword(s):
3D Cartography, Lidar Data, Occlusions, Multiple Views, Multiple Passages.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Incremental Learning
;
Pattern Recognition
;
Perception
;
Robotics
;
Software Engineering
;
Theory and Methods
Abstract:
Handling occlusions is one of the more difficult challenges faced today in urban landscape analysis and cartography. In this paper, we successfully address this problem by using a new method in which multiple views and multiple sessions or passages are used to complete occluded regions in a 3D cartographic map. Two 3D point clouds, from different viewing angles, obtained in each passage are first classified into two main object classes: Permanent and Temporary (which contains both Temporarily static and Mobile objects) using inference based on basic reasoning. All these Temporary objects, considered as occluding objects, are removed from the scene leaving behind two perforated 3D point clouds of the cartography. These two perforated point clouds from the same passage are then combined together to fill in some of the holes and form a unified perforated 3D point cloud of the cartography. This unified perforated 3D point cloud is then updated by similar subsequent perforated point cloud
s, obtained on different days and hours of the day, filling in the remaining holes and completing the missing features/regions of the urban cartography. This automatic method ensures that the resulting 3D point cloud of the cartography is most accurate containing only the exact and actual permanent features/regions. Special update and reset functions are added to increase the robustness of the method. The method is evaluated on a standard data set to demonstrate its efficacy and prowess.
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