3 September 2008 3D object matching on the GPU using spin-image surface matching
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Abstract
Spin-image surface matching is a technique for locating objects in a scene by processing three-dimensional surface information from sources such as light detection and ranging (LIDAR), structured light photography, and tomography. It is attractive for parallel processing on graphics processing units (GPUs) because the two main computational steps - matching pairs of spin-images by correlation, and matching pairs of points between model and scene - are explicitly parallel. By implementing these parallel computations on the GPU, as well as recasting serial portions of the algorithm into a parallel form and structuring the algorithm to limit data exchanges between host and GPU, this project achieved an overall speedup of 20 times or more compared to conventional serial processing. A demonstration application has been developed that allows users to select among a set of models and scenes and then applies the spin-image surface matching algorithm to match the selected models to the scene. It also has several user interface controls for changing parameters. One new parameter is a geometric consistency ratio (GCR) that quantifies the matching performance and provides a measure for discarding low-quality matches. By toggling between GPU- and host-based processing, the application demonstrates the speedup achieved with parallelization on the GPU.
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Nolan Davis, Dennis Braunreiter, Cezario Tebcherani, Masatoshi Tanida, "3D object matching on the GPU using spin-image surface matching", Proc. SPIE 7074, Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII, 707408 (3 September 2008); doi: 10.1117/12.801579; https://doi.org/10.1117/12.801579
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KEYWORDS
Algorithm development

Data modeling

Parallel processing

3D modeling

LIDAR

Control systems

Graphics processing units

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