Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Large-scale 3D scene modeling by registration of laser range data with Google maps images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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This paper presents a method for accurately segmenting and classifying 3D range data into particular object classes. Object classification of input images is necessary for applications including robot navigation and automation, in particular with respect to path planning. To achieve robust object classification, we propose the idea of an object feature which represents a distribution of neighboring points around a target point. In addition, rather than processing raw points, we reconstruct polygons from the point data, introducing connectivity to the points. With these ideas, we can refine the Markov Random Field (MRF) calculation with more relevant information with regards to determining "related points". The algorithm was tested against five outdoor scenes and provided accurate classification even in the presence of many classes of interest.