Active optical range imaging sensors
Advances in Machine Vision
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
New feature points based on geometric invariants for 3D image registration
International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Special issue on registration and fusion of range images
Computer Vision and Image Understanding - Registration and fusion of range images
The correspondence framework for 3D surface matching algorithms
Computer Vision and Image Understanding
| Hi-index | 0.01 |
Automatic coarse alignment, or registration, of partially overlapping three dimensional (3D) shapes is a fundamental problem of the shape acquisition and modelling pipeline. This paper describes a new approach to automatic coarse pair-wise registration of partially overlapping 3D point sets which are commonly generated by laser scanners, structured light systems or stereo. The approach is based on the characterization of the two 3D point clouds by sparse feature points invariant to Euclidean transformations, which are robustly extracted from the data by a multiscale procedure. The feature points are grouped in sets of triplets and the triplets are then characterized by signatures, which are vectors of parameters with the same invariance properties of the feature points. A matching procedure selects probable corresponding triplets in the signature space according to their signature distance. The alignment transformation able to register the two scan pairs is estimated from the matched triplets. A verification process asses the quality of match, which is defined as the number of feature points put in correct correspondence by the estimated transformation associated to a matched pair. This number of features must be greater than a prescribed threshold, defined by the expected percentage of overlapping of the two data sets. The proposed approach to registration has been evaluated on standard scan sets available on the web, and some preliminary results, presented and discussed in the paper, confirm its validity. Other work has to be done to analyze the robustness of the approach to noisy data and to optimize the computational time.