Perceptual organization and the representation of natural form
Artificial Intelligence
Segmentation and Classification of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
An optimization-based approach to the interpretation of single line drawings as 3D wire frames
International Journal of Computer Vision
Bayesian models, deformable templates and competitive priors
Proceedings of the 1991 York conference on Spacial vision in humans and robots
Segmentation of range images as the search for geometric parametric models
International Journal of Computer Vision
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Robust Segmentation of Primitives from Range Data in the Presence of Geometric Degeneracy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconstruction of Planar Surfaces Behind Occlusions in Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generic Methodology for Partitioning Unorganised 3D Point Clouds for Robotic Vision
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Range segmentation of large building exteriors: A hierarchical robust approach
Computer Vision and Image Understanding
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In this paper, we present a stochastic algorithm by effective Markov chain Monte Carlo (MCMC) for segmenting and reconstructing 3D scenes. The objective is to segment a range image and its associated reflectance map into a number of surfaces which fit to various 3D surface models and have homogeneous reflectance (material) properties. In comparison to previous work on range image segmentation, the paper makes the following contributions. Firstly, it is aimed at generic natural scenes, indoor and outdoor, which are often much complexer than most of the existing experiments in the "polyhedra world". Natural scenes require the algorithm to automatically deal with multiple types (families) of surface models which compete to explain the data. Secondly, it integrates the range image with the reflectance map. The latter provides material properties and is especially useful for surface of high specularity, such as glass, metal, ceramics. Thirdly, the algorithm is designed by reversible jump and diffusion Markov chain dynamics and thus achieves globally optimal solutions under the Bayesian statistical framework. Thus it realizes the cue integration and multiple model switching. Fourthly, it adopts two techniques to improve the speed of the Markov chain search: One is a coarse-to-fine strategy and the other are data driven techniques such as edge detection and clustering. The data driven methods provide important information for narrowing the search spaces in a probabilistic fashion. We apply the algorithm to two data sets and the experiments demonstrate robust and satisfactory results on both. Based on the segmentation results, we extend the reconstruction of surfaces behind occlusions to fill in the occluded parts.