Authors:
M. A. A. Rajput
1
;
E. Funk
1
;
A. Börner
1
and
O. Hellwich
2
Affiliations:
1
German Aerospace Center, Germany
;
2
Technical University Berlin, Germany
Keyword(s):
Large Scale Automated 3D Modelling, Mobile Robotics, Efficient Data Structures, 3D Database.
Related
Ontology
Subjects/Areas/Topics:
3D Virtual Environments and Surveillance Applications
;
Image and Video Processing, Compression and Segmentation
;
Multimedia
;
Multimedia Signal Processing
;
Multimedia Systems and Applications
;
Telecommunications
Abstract:
3D reconstruction from mobile image sensors is crucial for many offline-inspection and online robotic application.
While several techniques are known today to deliver high accuracy 3D models from images via
offline-processing, 3D reconstruction in real-time remains a major goal still to achieve. This work focuses on
incremental 3D modeling from error prone depth image data, since standard 3D fusion techniques are tailored
on accurate depth data from active sensors such as the Kinect. Imprecise depth data is usually provided by
stereo camera sensors or simultaneous localization and mapping (SLAM) techniques. This work proposes an
incremental extension of the total variation (TV) filtering technique, which is shown to reduce the errors of
the reconstructed 3D model by up to 77% compared to state of the art techniques.