3D Free-Form Object Recognition in Range Images Using Local Surface Patches

Authors:
Hui Chen;Bir Bhanu
Affiliations:
University of California, Riverside;University of California, Riverside
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Year:
2004

Citing 0
Cited 5

Using surface extended polar map (SEPMap) for surface matching and scale factor estimation

SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
3D free-form object recognition in range images using local surface patches

Pattern Recognition Letters
Local feature extraction and matching on range images: 2.5D SIFT

Computer Vision and Image Understanding
Viewpoint independent object recognition in cluttered scenes exploiting ray-triangle intersection and SIFT algorithms

Pattern Recognition
Surface registration using extended polar maps

ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces an integrated local surface descriptor for surface representation and object recognition. A local surface descriptor is defined by a centroid, its surface type and 2D histogram. The 2D histogram consists of shape indexes, calculated from principal curvatures, and angles between the normal of reference point and that of its neighbors. Instead of calculating local surface descriptors for all the 3D surface points, we only calculate them for feature points which are areas with large shape variation. Furthermore, in order to speed up the search process and deal with a large set of objects, model local surface patches are indexed into a hash table. Given a set of test local surface patches, we cast votes for models containing similar surface descriptors. Potential corresponding local surface patches and candidate models are hypothesized. Verification is performed by aligning models with scenes for the most likely models. Experiment results with real range data are presented to demonstrate the effectiveness of our approach.