Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Iterative point matching for registration of free-form curves and surfaces
International Journal of Computer Vision
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
COSMOS-A Representation Scheme for 3D Free-Form Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Shape Histograms for Similarity Search and Classification in Spatial Databases
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
Surface registration by matching oriented points
NRC '97 Proceedings of the International Conference on Recent Advances in 3-D Digital Imaging and Modeling
Matching 3D Models with Shape Distributions
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Harmonic shape images: a three-dimensional free-form surface representation and its applications in surface matching
A Survey of Content Based 3D Shape Retrieval Methods
SMI '04 Proceedings of the Shape Modeling International 2004
Salient geometric features for partial shape matching and similarity
ACM Transactions on Graphics (TOG)
Reassembling fractured objects by geometric matching
ACM SIGGRAPH 2006 Papers
Scale Selection for the Analysis of Point-Sampled Curves
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Hierarchical segmentation for unstructured and unfiltered range images
CGIV '07 Proceedings of the Computer Graphics, Imaging and Visualisation
The correspondence framework for 3D surface matching algorithms
Computer Vision and Image Understanding
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In this paper, we present a new algorithm for the alignment of two 3D scans. The approach uses a region-based matching technique. We make no assumptions about the initial positions of the scans. Regions are described by a probability density function (pdf) computed from low dimensional surface descriptors (curvature or normal cone). The algorithm allows registering directly raw noisy data, possibly with the presence of outliers, without any pre-processing, such as filtering, denoising, or reconstruction. Region correspondence is found using similarity function based on the comparison of regions pdf and under geometry constraints. Results on raw scan data sets are presented to illustrate and evaluate the algorithm.