Volume/surface octrees for the representation of three-dimensional objects
Computer Vision, Graphics, and Image Processing
Topics in matrix analysis
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
Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Rapid octree construction from image sequences
CVGIP: Image Understanding
How Far 3D Shapes Can Be Understood from 2D Silhouettes
IEEE Transactions on Pattern Analysis and Machine Intelligence
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Improving accuracy in a robust algorithm for three-dimensional Voronoi diagrams
Journal of Graphics Tools
The visualization toolkit (2nd ed.): an object-oriented approach to 3D graphics
The visualization toolkit (2nd ed.): an object-oriented approach to 3D graphics
Automatic Model Construction and Pose Estimation From Photographs Using Triangular Splines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photorealistic Scene Reconstruction by Voxel Coloring
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Constructing a Multivalued Representation for View Synthesis
International Journal of Computer Vision
Multi Viewpoint Stereo from Uncalibrated Video Sequences
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
The Modulus Constraint: A New Constraint for Self-Calibration
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
A Theory of Shape by Space Carving
A Theory of Shape by Space Carving
Geometric modeling for computer vision.
Geometric modeling for computer vision.
Silhouette and stereo fusion for 3D object modeling
Computer Vision and Image Understanding - Model-based and image-based 3D scene representation for interactive visalization
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Implicit Meshes for Effective Silhouette Handling
International Journal of Computer Vision
International Journal of Computer Vision
Robust Recovery of Shapes with Unknown Topology from the Dual Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volumetric Descriptions of Objects from Multiple Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of methods for volumetric scene reconstruction from photographs
VG'01 Proceedings of the 2001 Eurographics conference on Volume Graphics
3D modeling of multiple-object scenes from sets of images
Pattern Recognition
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In this paper, we present a semi-supervised approach to space carving by casting the recovery of volumetric data from multiple views into an evidence combining setting. The method presented here is statistical in nature and employs, as a starting point, a manually obtained contour. By making use of this user-provided information, we obtain probabilistic silhouettes of all successive images. These silhouettes provide a prior distribution that is then used to compute the probability of a voxel being carved. This evidence combining setting allows us to make use of background pixel information. As a result, our method combines the advantages of shape-from-silhouette techniques and statistical space carving approaches. For the carving process, we propose a new voxelated space. The proposed space is a projective one that provides a colour mapping for the object voxels which is consistent in terms of pixel coverage with their projection onto the image planes for the imagery under consideration. We provide quantitative results and illustrate the utility of the method on real-world imagery.