Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Range Image Segmentation Based on Differential Geometry: A Hybrid Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments in Curvature-Based Segmentation of Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
MIR: An Approach to Robust Clustering-Application to Range Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Range Image Segmentation Using High-Level Segmentation Primitives
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Architectural Modeling from Sparsely Scanned Range Data
International Journal of Computer Vision
Automatic segmentation of unorganized noisy point clouds based on the Gaussian map
Computer-Aided Design
3D line segment detection for unorganized point clouds from multi-view stereo
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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Object Segmentation is an important step in object reconstruction from point cloud data of complex urban scenes and in applications to virtual environment. This paper focuses on strategies to extract objects in 3D urban scenes for further object recognition and object reconstruction. Segmentation strategies are proposed according to object shape features. Rough segmentation is first adopted for objects classification, and further detailed segmentation is implemented for object components. Normal directions are adopted to segment each planar region, so that architectures and the ground can be extracted from other objects. Architectural components are further extracted through an analysis of planar residuals, and the residuals are used to choose seed points for region growing. And meanwhile, the size of segmental regions is used to determine whether or not it includes sparse noisy points. Experimental results on the scene scan data demonstrate that the proposed approach is effective in object segmentation, so that more details and more concise models can be obtained corresponding to real outdoor objects.