Reconstruction of sculpture from its profiles with unknown camera positions

Profiles of a sculpture provide rich information about its geometry, and can be used for shape recovery under known camera motion. By exploiting correspondences induced by epipolar tangents on the profiles, a successful solution to motion estimation from profiles has been developed in the special case of circular motion. The main drawbacks of using circular motion alone, namely the difficulty in adding new views and part of the object always being invisible, can be overcome by incorporating arbitrary general views of the object and registering its new profiles with the set of profiles resulted from the circular motion. In this paper, we describe a complete and practical system for producing a three-dimensional (3-D) model from uncalibrated images of an arbitrary object using its profiles alone. Experimental results on various objects are presented, demonstrating the quality of the reconstructions using the estimated motion.published_or_final_versio

Self-calibration of turntable sequences from silhouettes

This paper addresses the problem of recovering both the intrinsic and extrinsic parameters of a camera from the silhouettes of an object in a turntable sequence. Previous silhouette-based approaches have exploited correspondences induced by epipolar tangents to estimate the image invariants under turntable motion and achieved a weak calibration of the cameras. It is known that the fundamental matrix relating any two views in a turntable sequence can be expressed explicitly in terms of the image invariants, the rotation angle, and a fixed scalar. It will be shown that the imaged circular points for the turntable plane can also be formulated in terms of the same image invariants and fixed scalar. This allows the imaged circular points to be recovered directly from the estimated image invariants, and provide constraints for the estimation of the imaged absolute conic. The camera calibration matrix can thus be recovered. A robust method for estimating the fixed scalar from image triplets is introduced, and a method for recovering the rotation angles using the estimated imaged circular points and epipoles is presented. Using the estimated camera intrinsics and extrinsics, a Euclidean reconstruction can be obtained. Experimental results on real data sequences are presented, which demonstrate the high precision achieved by the proposed method. © 2009 IEEE.published_or_final_versio

Image-based rendering and synthesis

Multiview imaging (MVI) is currently the focus of some research as it has a wide range of applications and opens up research in other topics and applications, including virtual view synthesis for three-dimensional (3D) television (3DTV) and entertainment. However, a large amount of storage is needed by multiview systems and are difficult to construct. The concept behind allowing 3D scenes and objects to be visualized in a realistic way without full 3D model reconstruction is image-based rendering (IBR). Using images as the primary substrate, IBR has many potential applications including for video games, virtual travel and others. The technique creates new views of scenes which are reconstructed from a collection of densely sampled images or videos. The IBR concept has different classification such as knowing 3D models and the lighting conditions and be rendered using conventional graphic techniques. Another is lightfield or lumigraph rendering which depends on dense sampling with no or very little geometry for rendering without recovering the exact 3D-models.published_or_final_versio

Geometrische Autokalibrierung für die dentale Volumentomographie

Der technische Fortschritt und die damit einhergehende steigende Systemauflösung der Aufnahmegeräte machen die Bewegung des Patienten zu einem entscheidenden limitierenden Faktor der Bildqualität in der digitalen Volumentomographie (DVT). Patientenbewegungen verursachen schwere Bildartefakte im rekonstruierten Volumen und können dessen Diagnostizierbarkeit maßgeblich beeinträchtigen. Zur Korrektur von Patientenbewegungen können Autokalibrierverfahren eingesetzt werden, welche den Bewegungsfehler in den Bilddaten erkennen und automatisch korrigieren. Diese Arbeit behandelt die dentalspezifische Problemstellung einer Bewegungskorrektur für DVT. Aufgrund der geringen Strahlendosis und der Gerätegeometrie weisen dentale DVT-Daten verstärkt Bildartefakte auf. Dies verschärft die Rahmenbedingungen für Autokalibrierverfahren, da aufgrund der Bildartefakte der Bildentstehungsprozess nur ungenügend modelliert werden kann. Das erschwert eine isolierte Betrachtung des Bewegungsfehlers und somit die Auswertung der Datentreue von Projektionsdaten und rekonstruiertem Volumen. Diese Arbeit fokussiert sich daher auf merkmalbasierte Verfahren, welche eine Abstraktion des Autokalibrierproblems von den Bildartefakten ermöglichen. Konkret werden die Konturen der Zähne ausgewertet. Konturmerkmale sind zur Autokalibrierung dentaler DVT-Daten besonders geeignet, da ihre Dimensionalität und Ausprägung die Nachteile des hohen Rauschens und der Strukturüberlagerungen der Röntgenprojektionen kompensieren. Diese Arbeit umfasst die Beschreibung und Evaluation von drei neuen, auf dentale Daten zugeschnittenen Autokalibrierverfahren. Die Autokalibrierverfahren schätzen die Parameter der Projektionsgeometrie einer DVT-Aufnahme aus ihren Projektionsdaten. Sie behandeln Aspekte der Verfahrensrobustheit und der Reduktion der Problemkomplexität. Die Evaluation zeigt eine deutliche Verbesserung der Bewegungsartefakte und eine durchschnittliche Wiederherstellung der Bildschärfevonbiszu98%. Die Eignung konturbasierter Autokalibrierverfahren zur Kompensation von Patientenbewegungen in der dentalen DVT wird somit belegt

Camera calibration and configuration for estimation of tennis racket position in 3D.

Previously, stereo camera systems have been used to track markers attached to the racket frame, allowing for racket position to be measured in three-dimensions (3D). Typically, markers are manually selected on the image plane but this can be time consuming and inaccurate. The purpose of this project was to develop and validate a markerless method to estimate 3D racket position using a camera.The method relies on a silhouette of a racket captured with a camera whose relative pose (rotation and translation) is unknown. A candidate relative pose is used to measure the inconsistency between the silhouette and a set of racket silhouettes captured with a fully calibrated camera (known intrinsic and extrinsic parameters). The measure of inconsistency can be formulated as a cost function associated with the candidate relative pose. By adjusting parameters of the candidate relative pose to minimise the cost, an accurate estimation for the true 3D position of the racket can be made. Previous studies have found that silhouette-based pose optimisation methods depend on accurate camera calibration and silhouette extraction. Therefore, a repeatable and accurate camera calibration method to provide the relative pose of a camera with respect to a racket was developed. To facilitate silhouette extraction, the racket was painted black and a backlight was used.Synthetic camera poses and silhouette views associated with a 3D racket model were generated in Blender v2.70 and used to determine the optimum fully calibrated set configuration for a racket. A laboratory-based fully calibrated set (LFCS) consisting of 21 camera poses in a semispheric configuration was created. On average, using this set, racket position was reconstructed to within +/- 2 mm. This included systematic error arising from the calibration and error in the segmentation of silhouette boundaries. The maximum reconstruction error was 5.3 mm. Further synthetic testing demonstrated the methods ability to estimate 3D racket position during simulated real-play conditions. For racket silhouette orientations that simulated strokes expected to occur in tennis between 0 and 90°, mean RMSE for reconstruction of coordinates on the racket face plane was 1.5 +/- 1.8 mm. An RMSE of 2 mm was obtained from a camera positioned alongside the net, 14 m from the racket. Finally, this same camera position estimated 3D racket position to an accuracy of 1.9 +/- 0.14 mm using a fully calibrated set containing randomly orientated camera poses, during a simulated serve. This project developed and validated a novel markerless method to estimate 3D tennis racket position. A calibration method to obtain the relative pose of a camera with respect to a racket is presented and an appropriate configuration for a fully calibrated set is determined. The method has potential to be used alongside existing ball trajectory analysis tools to provide unprecedented information about player performance and to enhance tennis broadcasts. Future research should use the recommendations made in this project to inform and assist the development of the method for application during real tennis-play conditions