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
An automated registration method for range images
Edutainment'07 Proceedings of the 2nd international conference on Technologies for e-learning and digital entertainment
Real-time Object Recognition in Sparse Range Images Using Error Surface Embedding
International Journal of Computer Vision
Three-dimensional object registration using the wavelet transform
Proceedings of the 24th Spring Conference on Computer Graphics
Local shape descriptor selection for object recognition in range data
Computer Vision and Image Understanding
Scene parsing using a prior world model
International Journal of Robotics Research
Surface registration using extended polar maps
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
A neural network strategy for 3d surface registration
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
Adaptive background generation for video object segmentation
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Feature detection using curvature maps and the min-cut/max-flow algorithm
GMP'06 Proceedings of the 4th international conference on Geometric Modeling and Processing
Three-dimensional SLAM for mapping planetary work site environments
Journal of Field Robotics
A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes
International Journal of Computer Vision
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This paper proposes a new, efficient surface representation method for surface matching. A feature carrier for a surface point, which is a set of two-dimensional (2-D) contours that are the projections of geodesic circles on the tangent plane, is generated. The carrier is named point fingerprint because its pattern is similar to human fingerprints and plays a role in discriminating surface points. Corresponding points on surfaces from different views are found by comparing their fingerprints. The point fingerprint is able to carry curvature, color, and other information which can improve matching accuracy, and the matching process is faster than 2-D image comparison. A novel candidate point selection method based on the fingerprint irregularity is introduced. Point fingerprint is successfully applied to pose estimation of real range data.