Segmentation through Variable-Order Surface Fitting
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
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Shape modeling with point-sampled geometry
ACM SIGGRAPH 2003 Papers
Variational shape approximation
ACM SIGGRAPH 2004 Papers
A random sampling strategy for piecewise planar scene segmentation
Computer Vision and Image Understanding
Exploration trees on highly complex scenes: A new approach for 3D segmentation
Pattern Recognition
Superquadric Segmentation in Range Images via Fusion of Region and Boundary Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
People detection through quantified fuzzy temporal rules
Pattern Recognition
Spatial-Temporal Fusion for High Accuracy Depth Maps Using Dynamic MRFs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractional particle swarm optimization in multidimensional search space
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation
International Journal of Robotics Research
CC-RANSAC: Fitting planes in the presence of multiple surfaces in range data
Pattern Recognition Letters
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IEEE Transactions on Fuzzy Systems
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
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This paper presents an unsupervised planar segmentation algorithm of unorganized point clouds based on multidimensional (MD) particle swarm optimization (PSO). A robust objective function of the unsupervised planar segmentation is established according to clustering distances of PSO clustering algorithm and inliers of random sample consensus (RANSAC) method. After that, MD PSO algorithm is adopted to optimize the objective function, where the optimal number and positions of the segmented planar patches are sought simultaneously. In order not to get trapped in local optima, a modification strategy of the global best (GB) position of swarm in each dimension is added to the MD PSO algorithm. Thus the unsupervised planar segmentation of point clouds is realized. Experimental results demonstrate the high planar segmentation accuracy of the proposed algorithm.