Augmenting moving planar surfaces robustly with video projection and direct image alignment

Audet, Samuel; Okutomi, Masatoshi; Tanaka, Masayuki

doi:10.1007/s10055-012-0210-9

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…Marker Tracking: Color cameras are suitable for object tracking in real-time applications, exploiting fiducials [3] or an optical model of the object surface [8] to estimate the relative pose between the projector and target. Although many systems project contents on planar or quadratic surfaces using homography or quadratic parameterization to warp the contents, these typically assume a fairly simple surface structure.…”

Section: A Object Trackingmentioning

confidence: 99%

Digital Facial Augmentation for Interactive Entertainment

Hieda¹,

Cooperstock²

2015

Proceedings of the 7th International Conference on Intelligent Technologies for Interactive Entertainment

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Digital projection technology allows for effective and entertaining spatial augmented reality applications. Leveraging the capabilities of reasonably accurate object tracking using commodity cameras and/or depth sensors to determine the 3D position and pose of objects in real time, it is possible to project dynamic graphical content on arbitrary surfaces, such as a person's face. Coupling these capabilities with a simple drawing application, participants can have the experience of "painting" on someone's face, or even on their own, by observing the projection in a mirror. Similarly, integrating 2D rigid-body, fluid and gravity simulation, one may interact with virtual objects projected on their own face or body.

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Section: A Object Trackingmentioning

confidence: 99%

Digital Facial Augmentation for Interactive Entertainment

Hieda¹,

Cooperstock²

2015

Proceedings of the 7th International Conference on Intelligent Technologies for Interactive Entertainment

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“…Successful applications include the removal of shadows cast by the speaker during a presentation, by means of multiple projectors [6], combining multiple projectors to create large display walls [7], projection defocus analysis and correction [8,9], manipulating one object's colour and texture to make it look like another [10], and real-time correction of the geometry of the projected image according to the underlying geometry of the projection surface [11]. Audet [2,3] recently proposed an alignment strategy for SAR based on 4-point parametrisation, suggested by Baker et al [12], template matching and Gauss-Newton optimisation of the homography parameters. A calibration procedure was employed to model the intrinsics of the projector and camera, colour mixing matrix and colour offset.…”

Section: Related Workmentioning

confidence: 99%

“…This naturally results in two approaches: (1) treating the projector as interference or (2) modelling the projected image. The latter approach requires a-priori information about the reflectance properties of the surface and ambient illumination, which is obtained by means of some calibration procedure, as demonstrated by Audet et al [2,3]. Here, tracking was done by template matching, which assumes some continuous motion between frames or an educated guess on the motion parameters.…”

Section: Introductionmentioning

confidence: 99%

Colour Descriptors for Tracking in Spatial Augmented Reality

Kooi

Sorbier

Saitō

2013

Computer Vision - ACCV 2012 Workshops

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Abstract.Augmented Reality is an emerging research field, that aims for the composition of real and virtual imagery, by means of a camera and display device. Spatial augmented reality employs data projectors to augment the real world. In this setting, traditional tracking methods fall short due to the interference caused by the projector. Recent works assume a calibration process to model the projector and assume continuity in movement of the object being tracked. In this paper we present a tracking-by-detection system that does not require such a procedure and makes use of natural features represented by SIFT descriptors. We evaluate a set of photometric invariants that have previously been shown to improve the performance of object recognition, added to the descriptor to reduce the influence of the projector. We evaluate the descriptors based on precision-recall under projector distortion and the total system based on its tracking performance. Results show tracking is significantly more precise using one of the invariants.

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“…The closed-loop approach, by contrast, is a challenge owing to the difficulty of target surface tracking under interference by the projected content. A front door approach tackles this issue by incorporating the effect of projection into the optimization process for alignment purposes [1,2,22,33]. Adopting this approach to real-time tracking at a high frame rate, however, is difficult mainly because the optimization problem becomes complicated and intractable when the frame time is short.…”

Section: Introductionmentioning

confidence: 99%

Animated Stickies: Fast Video Projection Mapping onto a Markerless Plane through a Direct Closed-Loop Alignment

Kagami

Hashimoto

2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Snapshot sequences of dynamic projection mapping onto fast moving markerless surfaces. The bottom row indicates that moving the camera pose (left side in the photo) relative to the projector (right side) does not disturb the mapping.Abstract-This paper presents a fast projection mapping method for moving image content projected onto a markerless planar surface using a low-latency Digital Micromirror Device (DMD) projector. By adopting a closed-loop alignment approach, in which not only the surface texture but also the projected image is tracked by a camera, the proposed method is free from a calibration or position adjustment between the camera and projector. We designed fiducial patterns to be inserted into a fast flapping sequence of binary frames of the DMD projector, which allows the simultaneous tracking of the surface texture and a fiducial geometry separate from a single image captured by the camera. The proposed method implemented on a CPU runs at 400 fps and enables arbitrary video contents to be "stuck" onto a variety of textured surfaces.

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Augmenting moving planar surfaces robustly with video projection and direct image alignment

Cited by 9 publications

References 19 publications

Digital Facial Augmentation for Interactive Entertainment

Digital Facial Augmentation for Interactive Entertainment

Colour Descriptors for Tracking in Spatial Augmented Reality

Animated Stickies: Fast Video Projection Mapping onto a Markerless Plane through a Direct Closed-Loop Alignment

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