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
Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Robot Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
A review of recent range image registration methods with accuracy evaluation
Image and Vision Computing
Stereo Processing by Semiglobal Matching and Mutual Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally consistent 3D mapping with scan matching
Robotics and Autonomous Systems
A benchmark for 3D mesh segmentation
ACM SIGGRAPH 2009 papers
Performance Evaluation of Stereo Algorithms for Automotive Applications
ICVS '09 Proceedings of the 7th International Conference on Computer Vision Systems: Computer Vision Systems
Learning 3D mesh segmentation and labeling
ACM SIGGRAPH 2010 papers
A continuous max-flow approach to potts model
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Efficient large-scale stereo matching
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
The fusion of large scale classified side-scan sonar image mosaics
IEEE Transactions on Image Processing
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A method for segmenting three-dimensional data of underwater unstructured terrains is presented. The three-dimensional point clouds are converted to two-dimensional elevation maps and analyzed for segmentation in the frequency domain. The lower frequency components represent the slower varying undulations of the underlying ground. The cut-off frequency, below which the frequency components form the ground surface, is determined automatically using peak detection. The user can also specify a maximum admissible size of objects to drive the automatic detection of the cut-off frequency. The points above the estimated ground surface are clustered via standard proximity clustering to form object segments. The precision of the segmentation is compared against ground truth hand labelled data acquired by a stereo camera pair and a structured light sensor. It is also evaluated for registration error when the extracted segments are used for sub-map alignment. The proposed approach is compared to three point cloud based and two image based segmentation algorithms. The results show that the approach is applicable to a range of different terrains and is able to generate features useful for navigation.