Recovering Light Directions and Camera Poses from a Single Sphere

Wong, Kwan-Yee K.; Schnieders, Dirk; Li, Shuda

doi:10.1007/978-3-540-88682-2_48

Cited by 39 publications

(21 citation statements)

References 16 publications

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“…As the sphere projects onto a conic in the camera, its position can be reconstructed from the occluding contour of the imaged mirror by the algorithm described in [42]. Our method computes the position of a sphere from 2D-3D correspondences, and, thus, we expect results similar to those acquired by [42]. We used five images to verify this.…”

Section: Screen Positionmentioning

confidence: 70%

Corneal-Imaging Calibration for Optical See-Through Head-Mounted Displays

Plopski

Itoh

Nitschke

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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In recent years optical see-through head-mounted displays (OST-HMDs) have moved from conceptual research to a market of mass-produced devices with new models and applications being released continuously. It remains challenging to deploy augmented reality (AR) applications that require consistent spatial visualization. Examples include maintenance, training and medical tasks, as the view of the attached scene camera is shifted from the user's view. A calibration step can compute the relationship between the HMD-screen and the user's eye to align the digital content. However, this alignment is only viable as long as the display does not move, an assumption that rarely holds for an extended period of time. As a consequence, continuous recalibration is necessary. Manual calibration methods are tedious and rarely support practical applications. Existing automated methods do not account for user-specific parameters and are error prone. We propose the combination of a pre-calibrated display with a per-frame estimation of the user's cornea position to estimate the individual eye center and continuously recalibrate the system. With this, we also obtain the gaze direction, which allows for instantaneous uncalibrated eye gaze tracking, without the need for additional hardware and complex illumination. Contrary to existing methods, we use simple image processing and do not rely on iris tracking, which is typically noisy and can be ambiguous. Evaluation with simulated and real data shows that our approach achieves a more accurate and stable eye pose estimation, which results in an improved and practical calibration with a largely improved distribution of projection error.

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Section: Screen Positionmentioning

confidence: 70%

Corneal-Imaging Calibration for Optical See-Through Head-Mounted Displays

Plopski

Itoh

Nitschke

et al. 2015

IEEE Trans. Visual. Comput. Graphics

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“…The average diameter for the projected limbus is approximately 150 pixels ( Figure 6 (c)). We further took data sets for a spherical mirror with a radius of 7.9 mm that is similar in size to the corneal sphere, where we estimate the position of the mirror from its occluding contour [31]. Figure 6 shows the results.…”

Section: Methodsmentioning

confidence: 99%

Super-Resolution from Corneal Images

Nitschke

Nakazawa

2012

Procedings of the British Machine Vision Conference 2012

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The corneal imaging technique enables extraction of scene information from corneal reflections and realizes a large number of applications including environment map reconstruction and estimation of a person's area of view. However, since corneal reflection images are usually low quality and resolution, the outcome of the technique is currently limited. To overcome this issue, we propose a first non-central catadioptric approach to reconstruct high-resolution scene information from a series of lower resolution corneal images through a super-resolution technique. We describe a three-step process, including (1) single image environment map recovery, (2) multiple image registration, and (3) high-resolution image reconstruction. In a number of experiments we show that the proposed strategy successfully recovers high-frequency textures that are lost in the source images, and also works with other non-central catadioptric systems, e.g., involving spherical mirrors. The obtained information about a person and the environment enables novel applications, e.g., for surveillance systems, personal video, human-computer interaction, and upcoming head-mounted cameras (Google Glass [5]).

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“…However, once the physical radius of the sphere is given, the sphere can be uniquely located. We use the geometric method proposed in [20] to locate the center of the sphere. The basic idea of the method is to investigate the relationship between the general case where the sphere lies at an arbitrary position and the special case where the sphere lies on the z axis of the camera.…”

Section: The Projective Modelmentioning

confidence: 99%

“…We first use the Hough transform circle detection algorithm to detect a circle to approximate the conic section in the initial frame of the video stream and employ the algorithm in [20] to locate the center of the sphere. The rotation of the sphere is defined as follows: we define an object coordinate system in the center of the sphere.…”

Section: Detectionmentioning

confidence: 99%

An interactive handheld spherical 3D object display system

Wong

Leung

et al. 2011

Multimedia Systems

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Traditional display systems usually display 3D objects on static screens (monitor, wall, etc.) and the manipulation of virtual objects by the viewer is usually achieved via indirect tools such as keyboard or mouse. It would be more natural and direct if we display the object onto a handheld surface and manipulate it with our hands as if we were holding the real 3D object. In this paper, we propose a prototype system by projecting the object onto a handheld foam sphere. The aim is to develop an interactive 3D object manipulation and exhibition tool without the viewer having to wear spectacles. In our system, the viewer holds the sphere with his hands and moves it freely. Meanwhile we project well-tailored images onto the sphere to follow its motion, giving the viewer a virtual perception as if the object were sitting inside the sphere and being moved by the viewer. The design goal is to develop a lowcost, real-time, and interactive 3D display tool. An off-theshelf projector-camera pair is first calibrated via a simple but efficient algorithm. Vision-based methods are proposed to detect the sphere and track its subsequent motion. The projection image is generated based on the projective geometry among the projector, sphere, camera and the viewer. We describe how to allocate the view spot and warp the projection image. We also present the result and the performance evaluation of the system.

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Recovering Light Directions and Camera Poses from a Single Sphere

Cited by 39 publications

References 16 publications

Corneal-Imaging Calibration for Optical See-Through Head-Mounted Displays

Corneal-Imaging Calibration for Optical See-Through Head-Mounted Displays

Super-Resolution from Corneal Images

An interactive handheld spherical 3D object display system

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