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
A volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Geometry in C
Implementation techniques for geometric branch-and-bound matching methods
Computer Vision and Image Understanding
Salient geometric features for partial shape matching and similarity
ACM Transactions on Graphics (TOG)
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
Multi-scale features for approximate alignment of point-based surfaces
SGP '05 Proceedings of the third Eurographics symposium on Geometry processing
4-points congruent sets for robust pairwise surface registration
ACM SIGGRAPH 2008 papers
Integral invariants for robust geometry processing
Computer Aided Geometric Design
Global Optimization through Rotation Space Search
International Journal of Computer Vision
Stochastic global optimization for robust point set registration
Computer Vision and Image Understanding
Globally optimal consensus set maximization through rotation search
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
A Branch-and-Bound Approach to Correspondence and Grouping Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid
IEEE Transactions on Visualization and Computer Graphics
| Hi-index | 0.00 |
Our goal is the registration of multiple 3D point clouds obtained from LIDAR scans of underground mines. Such a capability is crucial to the surveying and planning operations in mining. Often, the point clouds only partially overlap and initial alignment is unavailable. Here, we propose an interactive user-assisted point cloud registration system. Guided by the system, the user's role is simply to identify and search for overlapping regions across the point clouds. Specifically, given two point sets, the user clicks on a point in one set, then simply hovers the mouse on the other set to find a matching point. Each mouse position gives rise to a translation, and our system instantly optimises the rotation that aligns the point clouds. Assuming that each individual point set is horizontally levelled with the ground by the level compensator on the LIDAR device, given a candidate 3D translation only one angular parameter needs to be estimated to rotationally align two 3D point sets. We propose a fast rotation search algorithm that delivers globally optimal results in real time. Our method conducts branch-and-bound optimisation with a novel bounding function whose evaluation amounts to simple sorted array operations. This provides smooth and accurate feedback to the user's search for overlapping regions. Our system is intuitive and helps to accelerate the registration of multiple scans.