Issue No. 05 - May (2013 vol. 35)
Yan Cui , Augmented Vision, German Res. Center for Artificial Intell., Kaiserslautern, Germany
S. Schuon , Stylight GmbH, Munich, Germany
S. Thrun , Comput. Sci. Dept., Stanford Univ., Stanford, CA, USA
D. Stricker , Augmented Vision, German Res. Center for Artificial Intell., Kaiserslautern, Germany
C. Theobalt , MPI Inf., Saarbrucken, Germany
We describe a method for 3D object scanning by aligning depth scans that were taken from around an object with a Time-of-Flight (ToF) camera. These ToF cameras can measure depth scans at video rate. Due to comparably simple technology, they bear potential for economical production in big volumes. Our easy-to-use, cost-effective scanning solution, which is based on such a sensor, could make 3D scanning technology more accessible to everyday users. The algorithmic challenge we face is that the sensor's level of random noise is substantial and there is a nontrivial systematic bias. In this paper, we show the surprising result that 3D scans of reasonable quality can also be obtained with a sensor of such low data quality. Established filtering and scan alignment techniques from the literature fail to achieve this goal. In contrast, our algorithm is based on a new combination of a 3D superresolution method with a probabilistic scan alignment approach that explicitly takes into account the sensor's noise characteristics.
Cameras, Image resolution, Shape, Image reconstruction, Noise, Solid modeling, Systematics, Kinect, Superresolution, global alignment, rigid transformation, nonrigid transformation, 3D scanning, time-of-flight
C. Theobalt, S. Thrun, D. Stricker, Yan Cui and S. Schuon, "Algorithms for 3D Shape Scanning with a Depth Camera," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. , pp. 1039-1050, 2013.