Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Photorealistic Scene Reconstruction by Voxel Coloring
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-View Stereo via Volumetric Graph-Cuts
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Surface Reconstruction Method Using Global Graph Cut Optimization
International Journal of Computer Vision
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
| Hi-index | 0.00 |
Multi-view scene reconstruction from multiple uncalibrated images can be solved by two stages of processing: first, a sparse reconstruction using Structure From Motion (SFM), and second, a surface reconstruction using optimization of Markov random field (MRF). This paper focuses on the second step, assuming that a set of sparse feature points have been reconstructed and the cameras have been calibrated by SFM. The multi-view surface reconstruction is formulated as an image-based multi-labeling problem solved using MRF optimization via graph cut. First, we construct a 2D triangular mesh on the reference image, based on the image segmentation results provided by an existing segmentation process. By doing this, we expect that each triangle in the mesh is well aligned with the object boundaries, and a minimum number of triangles are generated to represent the 3D surface. Second, various objective and heuristic depth cues such as the slanting cue, are combined to define the local penalty and interaction energies. Third, these local energies are adapted to the local image content, based on the results from some simple content analysis techniques. The experimental results show that the proposed method is able to well the preserve the depth discontinuity because of the image content adaptive local energies.