A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Determining motion from 3D line segment matches: a comparative study
Image and Vision Computing
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
Object modelling by registration of multiple range images
Image and Vision Computing - Special issue: range image understanding
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
An optimal algorithm for approximate nearest neighbor searching
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Algorithms for Matching 3D Line Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Modeling with a Hand-Held Camera
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Range Image Segmentation by an Effective Jump-Diffusion Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Automatic Range to Range Registration: A Feature-Based Method
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Fully Automatic Registration of 3D Point Clouds
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Range Image Registration Based on Circular Features
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Robust multi-view feature matching from multiple unordered views
Pattern Recognition
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Modeling and rendering from multiple views
Modeling and rendering from multiple views
4-points congruent sets for robust pairwise surface registration
ACM SIGGRAPH 2008 papers
Range segmentation of large building exteriors: A hierarchical robust approach
Computer Vision and Image Understanding
Complex and photo-realistic scene representation based on range planar segmentation and model fusion
International Journal of Robotics Research
3D modeling from multiple images
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Complex and photo-realistic scene representation based on range planar segmentation and model fusion
International Journal of Robotics Research
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We present a common framework for accurate and automatic registration of two geometrically complex 3D range scans by using linear or planar features. The linear features of a range scan are extracted with an efficient split-and-merge line-fitting algorithm, which refines 2D edges extracted from the associated reflectance image considering the corresponding 3D depth information. The planar features are extracted employing a robust planar segmentation method, which partitions a range image into a set of planar patches. We propose an efficient probability-based RANSAC algorithm to automatically register two overlapping range scans. Our algorithm searches for matching pairs of linear (planar) features in the two range scans leading to good alignments. Line orientation (plane normal) angles and line (plane) distances formed by pairs of linear (planar) features are invariant with respect to the rigid transformation and are utilized to find candidate matches. To efficiently seek for candidate pairs and groups of matched features we build a fast search codebook. Given two sets of matched features, the rigid transformation between two scans is computed by using iterative linear optimization algorithms. The efficiency and accuracy of our registration algorithm were evaluated on several challenging range data sets.