Neumann D, Lugauer F, Bauer S, Wasza J, Hornegger J (2011)
Publication Type: Conference contribution, Conference Contribution
Publication year: 2011
Original Authors: Neumann D., Lugauer F., Bauer S., Wasza J., Hornegger J.
Book Volume: null
Pages Range: 1161-1167
Event location: Barcelona
Journal Issue: null
DOI: 10.1109/ICCVW.2011.6130381
The modeling of three-dimensional scene geometry from temporal point cloud streams is of particular interest for a variety of computer vision applications. With the advent of RGB-D imaging devices that deliver dense, metric and textured 6-D data in real-time, on-the-fly reconstruction of static environments has come into reach. In this paper, we propose a system for real-time point cloud mapping based on an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). In order to achieve robust mappings at real-time performance, our nearest neighbor search evaluates both geometric and photometric information in a direct manner. For acceleration of the search space traversal, we exploit the inherent computing parallelism of GPUs. In this work, we have investigated the fitness of the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, for 6-D data. In particular, we introduce a scheme that enables both fast RBC construction and queries. The proposed system is validated on an indoor scene modeling scenario. For dense data from the Microsoft Kinect sensor (640×480 px), our implementation achieved ICP runtimes of< 20 ms on an off-the-shelf consumer GPU. © 2011 IEEE.
APA:
Neumann, D., Lugauer, F., Bauer, S., Wasza, J., & Hornegger, J. (2011). Real-time RGB-D mapping and 3-D modeling on the GPU using the random ball cover data structure. In Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 (pp. 1161-1167). Barcelona.
MLA:
Neumann, Dominik, et al. "Real-time RGB-D mapping and 3-D modeling on the GPU using the random ball cover data structure." Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011, Barcelona 2011. 1161-1167.
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