Robust online 3D reconstruction combining a depth sensor and sparse feature points
Paper in proceeding, 2016

Online 3D reconstruction has been an active research area for a long time. Since the release of the Microsoft Kinect Camera and publication of KinectFusion [11] attention has been drawn how to acquire dense models in real-time. In this paper we present a method to make online 3D reconstruction which increases robustness for scenes with little structure information and little texture information. It is shown empirically that our proposed method also increases robustness when the distance between the camera positions becomes larger than what is commonly assumed. Quantitative and qualitative results suggest that this approach can handle situations where other well-known methods fail. This is important in, for example, robotics applications like when the camera position and the 3D model must be created online in real-time.

Author

E. Bylow

Lund University

Claes Olsson

Lund University

Fredrik Kahl

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

Proceedings - International Conference on Pattern Recognition

10514651 (ISSN)

Vol. 0 3709-3714
978-1-5090-4847-2 (ISBN)

Subject Categories

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1109/ICPR.2016.7900211

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Latest update

7/12/2024