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
Zippered polygon meshes from range images
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
A robust method for registration and segmentation of multiple range images
Computer Vision and Image Understanding
Point Signatures: A New Representation for 3D Object Recognition
International Journal of Computer Vision
Robust motion and correspondence of noisy 3-D point sets with missing data
Pattern Recognition Letters
Algorithms and Theory of Computation Handbook
Algorithms and Theory of Computation Handbook
Genetic Algorithms for Free-Form Surface Matching
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
International Journal of Computer Vision
Three-Dimensional Model-Based Object Recognition and Segmentation in Cluttered Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A review of recent range image registration methods with accuracy evaluation
Image and Vision Computing
4-points congruent sets for robust pairwise surface registration
ACM SIGGRAPH 2008 papers
A high-accuracy method for fine registration of overlapping point clouds
Image and Vision Computing
Hi-index | 0.10 |
Point cloud matching is a central problem in Object Modeling with applications in Computer Vision and Computer Graphics. Although the problem is well studied in the case when an initial estimate of the relative pose is known (fine matching), the problem becomes much more difficult when this a priori knowledge is not available (coarse matching). In this paper we introduce a novel technique to speed up coarse matching algorithms for point clouds. This new technique, called Hierarchical Normal Space Sampling (HNSS), extends Normal Space Sampling by grouping points hierarchically according to the distribution of their normal vectors. This hierarchy guides the search for corresponding points while staying free of user intervention. This permits to navigate through the huge search space taking advantage of geometric information and to stop when a sufficiently good initial pose is found. This initial pose can then be used as the starting point for any fine matching algorithm. Hierarchical Normal Space Sampling is adaptable to different searching strategies and shape descriptors. To illustrate HNSS, we present experiments using both synthetic and real data that show the computational complexity of the problem, the computation time reduction obtained by HNSS and the application potentials in combination with ICP.