Zippered polygon meshes from range images
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Markov random field modeling in computer vision
Markov random field modeling in computer vision
A volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Registration and Integration of Multiple Object Views for 3D Model Construction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time 3D model acquisition
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
A Theory of Shape by Space Carving
International Journal of Computer Vision - Special issue on Genomic Signal Processing
Markov random field modeled range image segmentation
Pattern Recognition Letters
Range Image Fusion for Object Reconstruction and Modeling
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Adaptively Merging Large-Scale Range Data with Reflectance Properties
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
A volumetric fusion technique for surface reconstruction from silhouettes and range data
Computer Vision and Image Understanding
Surface modeling using multi-view range and color images
Integrated Computer-Aided Engineering
Accurate integration of multi-view range images using k-means clustering
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A clustering approach to free form surface reconstruction from multi-view range images
Image and Vision Computing
Automatic 3d free form shape matching using the graduated assignment algorithm
Pattern Recognition
Hi-index | 0.00 |
Multi-view range image integration aims at producing a single reasonable 3D point cloud. The point cloud is likely to be inconsistent with the measurements topologically and geometrically due to registration errors and scanning noise. This paper proposes a novel integration method cast in the framework of Markov random fields (MRF). We define a probabilistic description of a MRF model designed to represent not only the interpoint Euclidean distances but also the surface topology and neighbourhood consistency intrinsically embedded in a predefined neighbourhood. Subject to this model, points are clustered in aN iterative manner, which compensates the errors caused by poor registration and scanning noise. The integration is thus robust and experiments show the superiority of our MRF-based approach over existing methods.