Generating octree models of 3D objects from their silhouettes in a sequence of images
Computer Vision, Graphics, and Image Processing
Computational geometric methods in volumetric intersection for 3d reconstruction
Pattern Recognition
How Far 3D Shapes Can Be Understood from 2D Silhouettes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Model Construction and Pose Estimation From Photographs Using Triangular Splines
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polyhedral Visual Hulls for Real-Time Rendering
Proceedings of the 12th Eurographics Workshop on Rendering Techniques
The Amsterdam Library of Object Images
International Journal of Computer Vision
Parallel controllable texture synthesis
ACM SIGGRAPH 2005 Papers
Multiview 3D Tracking with an Incrementally Constructed 3D Model
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Efficient Polyhedral Modeling from Silhouettes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A 3D Face Model for Pose and Illumination Invariant Face Recognition
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Smooth Kernel Density Estimate for Multiple View Reconstruction
CVMP '10 Proceedings of the 2010 Conference on Visual Media Production
Volumetric Descriptions of Objects from Multiple Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian 3D shape from silhouettes
Digital Signal Processing
| Hi-index | 0.00 |
This paper extends the likelihood kernel density estimate of the visual hull proposed by Kim et al [1] by introducing a prior. Inference of the shape is performed using a meanshift algorithm over a posterior kernel density function that is refined iteratively using both a multiresolution framework (to avoid local maxima) and using KNN for selecting the best reconstruction basis at each iteration. This approach allows us to recover concave areas of the shape that are usually lost when estimating the visual hull.