Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning associative Markov networks
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale Selection for Classification of Point-Sampled 3-D Surfaces
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Scale Selection for the Analysis of Point-Sampled Curves
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Onboard contextual classification of 3-D point clouds with learned high-order Markov random fields
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Robust principal component analysis?
Journal of the ACM (JACM)
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The classification of 3d point cloud data is an important component of applications such as map generation and architectural modeling. However, the complexity of the scenes together with the level of noise in the data acquired through mobile laser range-scanning make this task quite difficult. We propose a novel classification method that relies on a combination of edge, node, and relative density information within an Associative Markov Network framework. The main application of our work is the classification of the structures within a point cloud into curvilinear, surface-like, and noise components. We are able to robustly extract complicated structures such as tree branches. The measures taken to ensure the robustness of our method generalize and can be leveraged in noise reduction applications as well. We compare our work with another state of the art classification technique, namely Directional Associative Markov Network, and show that our method can achieve significantly higher accuracy in the classification of the 3d point clouds.