Using vanishing points for camera calibration
International Journal of Computer Vision
Fitting Parameterized Three-Dimensional Models to Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Euclidean 3D Reconstruction from Image Sequences with Variable Focal Lenghts
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Projective geometry and photometry for object detection and delineation
Projective geometry and photometry for object detection and delineation
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Using Geometric Constraints through Parallelepipeds for Calibration and 3D Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least-squares 3D reconstruction from one or more views and geometric clues
Computer Vision and Image Understanding
Extraction, matching, and pose recovery based on dominant rectangular structures
Computer Vision and Image Understanding
Reconstruction of linearly parameterized models using the vanishing points from a single image
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Direct Calibration by Fitting of Cuboids to a Single Image Using Differential Evolution
International Journal of Computer Vision
Stabilizing 3D modeling with geometric constraints propagation
Computer Vision and Image Understanding
Least-squares 3D reconstruction from one or more views and geometric clues
Computer Vision and Image Understanding
Extraction, matching, and pose recovery based on dominant rectangular structures
Computer Vision and Image Understanding
3D primitive reconstruction using the line segment with single image
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartIII
Reconstruction of linearly parameterized models from a single image using the vanishing points
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Perceptual depth estimation from a single 2d image based on visual perception theory
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Precise 3d reconstruction from a single image
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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This paper deals with the problem of recovering the dimensions of an object and its pose from a single image acquired with a camera of unknown focal length. It is assumed that the object in question can be modeled as a polyhedron where the coordinates of the vertices can be expressed as a linear function of a dimension vector, $\lambda$. The reconstruction program takes as input, a set of correspondences between features in the model and features in the image. From this information, the program determines an appropriate projection model for the camera (scaled orthographic or perspective), the dimensions of the object, its pose relative to the camera and, in the case of perspective projection, the focal length of the camera. This paper describes how the reconstruction problem can be framed as an optimization over a compact set with low dimension驴no more than four. This optimization problem can be solved efficiently by coupling standard nonlinear optimization techniques with a multistart method which generates multiple starting points for the optimizer by sampling the parameter space uniformly. The result is an efficient, reliable solution system that does not require initial estimates for any of the parameters being estimated.