Using vanishing points for camera calibration
International Journal of Computer Vision
Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Tour into the picture: using a spidery mesh interface to make animation from a single image
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Euclidean structure from confocal conics: Theory and application to camera calibration
Computer Vision and Image Understanding
Euclidean structure from N ≥ 2 parallel circles: theory and algorithms
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Interactive object modelling based on piecewise planar surface patches
Computer Vision and Image Understanding
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Metric reconstruction of a projected plane is equivalent to estimating metric invariants of the plane in projective space. Working with an unknown planar scene taken with an uncalibrated camera, however, estimating the metric invariants may not be possible only with features on the plane, because the human visual system is not good at detecting sufficient information. Although the conventional algorithms use only features on the plane to recover the plane metric, we show that features not on the plane can be utilized. Study about the range of the dual conics and its self-polar triangle verifies that the metric invariant, the conic dual to the circular points of a plane is constrained with the orthogonal vanishing points under the assumption of the same camera. Using the constraint, we can get the orthogonal vanishing points from the given metric invariants, or inversely, the metric invariants from the orthogonal vanishing points. We also show that an image warping based on parallelism and orthogonality gives a physically meaningful parameterization of the metric invariant. With this new parameterization and the self-polar constraint, it is possible to recover the metric of a plane from information that the human visual system can easily detect without explicit camera calibration.