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Real-time 3D point cloud segmentation using Growing Neural Gas with Utility
2016
2016 9th International Conference on Human System Interactions (HSI)
This paper proposes a real-time feature extraction and segmentation method for a 3D point cloud. First of all, we apply Growing Neural Gas with Utility (GNG-U) to the point cloud for learning a topological structure. However, the standard GNG-U cannot learn the topological structure of 3D space environment and color information simultaneously. To this end, we then modify the GNG-U algorithm by using a weight vector. we propose a surface feature extraction and segmentation method by efficiently
doi:10.1109/hsi.2016.7529667
dblp:conf/hsi/TodaJYTWK16
fatcat:5uomwrodz5ajrm43ebbm32oh5m