Silhouette Coherence for Camera Calibration under Circular Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A random sampling strategy for piecewise planar scene segmentation
Computer Vision and Image Understanding
Robust multi-view feature matching from multiple unordered views
Pattern Recognition
Image-Based Modeling by Joint Segmentation
International Journal of Computer Vision
Dense height map estimation from oblique aerial image sequences
Computer Vision and Image Understanding
An occupancy-depth generative model of multi-view images
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Temporal priors for novel video synthesis
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
A polygon soup representation for multiview coding
Journal of Visual Communication and Image Representation
Colour volumetric compression for realistic view synthesis applications
Multimedia Tools and Applications
Editor's Choice Article: Video-based, real-time multi-view stereo
Image and Vision Computing
Recovery and Reasoning About Occlusions in 3D Using Few Cameras with Applications to 3D Tracking
International Journal of Computer Vision
Dense stereo by triangular meshing and cross validation
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
3D modeling from multiple images
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
3D Scene Reconstruction from Multiple Spherical Stereo Pairs
International Journal of Computer Vision
| Hi-index | 0.00 |
This paper addresses the problem of reconstructing the geometry and color of a Lambertian scene, given some fully calibrated images acquired with wide baselines. In order to completely model the input data, we propose to represent the scene as a set of colored depth maps, one per input image. We formulate the problem as a Bayesian MAP problem which leads to an energy minimization method. Hidden visibility variables are used to deal with occlusion, reflections and outliers. The main contributions of this work are: a prior for the visibility variables that treats the geometric occlusions; and a prior for the multiple depth maps model that smoothes and merges the depth maps while enabling discontinuities. Real world examples showing the efficiency and limitations of the approach are presented.