Proceedings of the 27th annual conference on Computer graphics and interactive techniques
The Visual Hull Concept for Silhouette-Based Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polyhedral Visual Hulls for Real-Time Rendering
Proceedings of the 12th Eurographics Workshop on Rendering Techniques
Geometric modeling for computer vision.
Geometric modeling for computer vision.
Surface Reconstruction from Feature Based Stereo
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Visibility Constrained Surface Evolution
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Fusion of Multi-View Silhouette Cues Using a Space Occupancy Grid
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Image-based multi-view scene analysis using 'conexels'
VisHCI '06 Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56
Shape from inconsistent silhouette
Computer Vision and Image Understanding
Efficient Polyhedral Modeling from Silhouettes
IEEE Transactions on Pattern Analysis and Machine Intelligence
An occupancy-depth generative model of multi-view images
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Shape from Silhouette Consensus
Pattern Recognition
Hi-index | 0.00 |
Traditional shape from silhouette methods compute the 3D shape as the intersection of the back-projected silhouettes in the 3D space, the so called visual hull. However, silhouettes that have been obtained with background subtraction techniques often present miss-detection errors (produced by false negatives or occlusions) which produce incomplete 3D shapes. Our approach deals with miss-detections, false alarms, and noise in the silhouettes. We recover the voxel occupancy which describes the 3D shape by minimizing an energy based on an approximation of the error between the shape 2D projections and the silhouettes. Two variants of the projection - and as a result the energy - as a function of the voxel occupancy are proposed. One of these variants outperforms the other. The energy also includes a sparsity measure, a regularization term, and takes into account the visibility of the voxels in each view in order to handle self-occlusions.