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Abstract

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.

Keywords

scale-space, point cloud analysis, interest regions, multi-scale representation

Notes

Sponsor: Army Research Laboratory

Grant ID: DAAD19-01-209912

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Field Robotics Center

Associated Project(s): CTA Robotics

Number of pages: 8

Text Reference

Ranjith Unnikrishnan and Martial Hebert, "Multi-Scale Interest Regions from Unorganized Point Clouds," Workshop on Search in 3D (S3D), IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June, 2008.

BibTeX Reference

@inproceedings{Unnikrishnan_2008_6111,
   author = "Ranjith Unnikrishnan and Martial Hebert",
   title = "Multi-Scale Interest Regions from Unorganized Point Clouds",
   booktitle = "Workshop on Search in 3D (S3D), IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)",
   month = "June",
   year = "2008",
}

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