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Robust online 3D reconstruction combining a depth sensor and sparse feature points

E. Bylow ; C. Olsson ; Fredrik Kahl (Institutionen för signaler och system, Bildanalys och datorseende)
Proceedings - 23rd International Conference on Pattern Recognition, ICPR 2016, Cancun, Mexico, 4-8 December 2016 (1051-4651). p. 3709-3714. (2017)
[Konferensbidrag, refereegranskat]

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.

Denna post skapades 2017-06-01. Senast ändrad 2017-10-10.
CPL Pubid: 249553


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Institutioner (Chalmers)

Institutionen för signaler och system, Bildanalys och datorseende (2013-2017)


Datorseende och robotik (autonoma system)

Chalmers infrastruktur