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IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
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ACM Transactions on Graphics (TOG)
Conditional Random Field for 3D Point Clouds with Adaptive Data Reduction
CW '07 Proceedings of the 2007 International Conference on Cyberworlds
Architectural Modeling from Sparsely Scanned Range Data
International Journal of Computer Vision
SmartBoxes for interactive urban reconstruction
ACM SIGGRAPH 2010 papers
Automatic reconstruction of tree skeletal structures from point clouds
ACM SIGGRAPH Asia 2010 papers
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This paper presents a system that can automatically segment objects in large scale 3D point clouds obtained from urban ranging images. The system consists of three steps: The first one involves a ground detection process that can detect relatively complex terrain and separate it from other objects. The second step superpixelizes the remaining objects to speed up the segmentation process. In the final step, a manifold embedded mode seeking method is adopted to segment the point clouds. Even though the segmentation of urban objects is a challenging problem in terms of accuracy and problem scale, our system can efficiently generate very good segmentation results. The proposed manifold learning effectively improves the segmentation performance due to the fact that continuous artificial objects often have manifold-like structures.