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Multi-Scale Interest Regions from Unorganized Point Clouds

Ranjith Unnikrishnan and Martial Hebert
Conference Paper, Carnegie Mellon University, Workshop on Search in 3D (S3D), IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June, 2008

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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.

BibTeX Reference
title = {Multi-Scale Interest Regions from Unorganized Point Clouds},
author = {Ranjith Unnikrishnan and Martial Hebert},
booktitle = {Workshop on Search in 3D (S3D), IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
keyword = {scale-space, point cloud analysis, interest regions, multi-scale representation},
sponsor = {Army Research Laboratory},
grantID = {DAAD19-01-209912},
school = {Robotics Institute , Carnegie Mellon University},
month = {June},
year = {2008},
address = {Pittsburgh, PA},