Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Geometric invariance in computer vision
Geometric invariance in computer vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Canonic representations for the geometries of multiple projective views
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
A Paraperspective Factorization Method for Shape and Motion Recovery
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Synthesizing realistic facial expressions from photographs
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Intensity- and Gradient-Based Stereo Matching Using Hierarchical Gaussian Basis Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inference of Integrated Surface, Curve, and Junction Descriptions From Sparse 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Automatic Isosurface Propagation Using an Extrema Graph and Sorted Boundary Cell Lists
IEEE Transactions on Visualization and Computer Graphics
Occlusions, Discontinuities, and Epipolar Lines in Stereo
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Inferring Segmented Surface Description from Stereo Data
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Semi-Automatic System to Infer Compex 3-D Shapes from Photographs
ICMCS '99 Proceedings of the IEEE International Conference on Multimedia Computing and Systems - Volume 2
A Maximum-Flow Formulation of the N-Camera Stereo Correspondence Problem
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Video Database of Moving Faces and People
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching 2.5D Face Scans to 3D Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time facial expression recognition using STAAM and layered GDA classifier
Image and Vision Computing
Visual traversability analysis for micro planetary rover
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Efficient normalized cross correlation calculation method for stereo vision based robot navigation
Frontiers of Computer Science in China
Which stereo matching algorithm for accurate 3d face creation ?
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
What is the average human face?
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
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This paper presents a working system for building 3-D human face models from two photographs. Rather than using expensive 3-D scanners, we show that frontal face models can be faithfully reconstructed from two photographs taken by consumer digital cameras in a totally non-invasive setup. We first rectify the image pair so that corresponding epipolar lines become coincident, by computing a dual point transformation. We then address the correspondence problem by converting it into a maximal surface extraction problem, which is then solved efficiently. The method effectively removes local extrema. Finally, a Euclidean reconstruction is achieved with the help of a novel factorization method for perspective cameras. Most of the computational steps are conducted in projective space. Euclidean information is introduced only at the last stage. This sets apart our system from the traditional ones which begin with metric information by using carefully calibrated cameras. We have collected a bank of face pairs to test our system, and are satisfied with its performance. Results from this image database are demonstrated.