An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
The Role of Model-Based Segmentation in the Recovery of Volumetric Parts From Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Part Segmentation Using Simulated Electrical Charge Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation and comparison of different segmentation algorithms
Pattern Recognition Letters
Robust Adaptive Segmentation of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Vision for Complete Scene Reconstruction and Exploration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraint-Based Sensor Planning for Scene Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Range image segmentation of scenes with occluded curved objects
Pattern Recognition Letters
Robust Segmentation of Primitives from Range Data in the Presence of Geometric Degeneracy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Range Segmentation Using Visibility Constraints
International Journal of Computer Vision
3D Complex Scenes Segmentation from a Single Range Image Using Virtual Exploration
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Segmentation of Range Images through the Integration of Different Strate Gies
VMV '01 Proceedings of the Vision Modeling and Visualization Conference 2001
Gradient-based polyhedral segmentation for range images
Pattern Recognition Letters
3D scene analysis from a single range image through occlusion graphs
Pattern Recognition Letters
Analyzing DGI-BS: properties and performance under occlusion and noise
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
3D scene retrieval and recognition with Depth Gradient Images
Pattern Recognition Letters
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A new strategy for automatic object extraction in highly complex scenes is presented in this paper. The method proposed gives a solution for 3D segmentation avoiding most restrictions imposed in other techniques. Thus, our technique is applicable on unstructured 3D information (i.e. cloud of points), with a single view of the scene, scenes consisting of several objects where contact, occlusion and shadows are allowed, objects with uniform intensity/texture and without restrictions of shape, pose or location. In order to have a fast segmentation stopping criteria, the number of objects in the scene is taken as input. The method is based on a new distributed segmentation technique that explores the 3D data by establishing a set of suitable observation directions. For each exploration viewpoint, a strategy [3D data]-[2D projected data]-[2D segmentation]-[3D segmented data] is accomplished. It can be said that this strategy is different from current 3D segmentation strategies. This method has been successfully tested in our lab on a set of real complex scenes. The results of these experiments, conclusions and future improvements are also shown in the paper.