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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Multi-scale interest regions from unorganized point clouds
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Ranjith Unnikrishnan, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Martial Hebert, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Several computer vision algorithms rely on detecting a compact but representative set of interest regions and their associated descriptors from input data. When the input is in the form of an unorganized 3D point cloud, current practice is to compute shape descriptors either exhaustively or at randomly chosen locations using one or more preset neighborhood sizes. Such a strategy ignores the relative variation in the spatial extent of geometric structures and also risks introducing redundancy in the representation. This paper pursues multi-scale operators on point clouds that allow detection of interest regions whose locations as well as spatial extent are completely data-driven. The approach distinguishes itself from related work by operating directly in the input 3D space without assuming an available polygon mesh or resorting to an intermediate global 2D parameterization. Results are shown to demonstrate the utility and robustness of the proposed method.
Citation:
Ranjith Unnikrishnan, Martial Hebert, "Multi-scale interest regions from unorganized point clouds," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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