The Frankencamera

Adams, Andrew; Horowitz, Mark; Park, Sung Hee; Gelfand, Natasha; Baek, Jiwon; Matusik, Wojciech; Levoy, Marc; Jacobs, David E.; Dolson, Jennifer; Tico, Marius; Pulli, Kari; Talvala, Eino-Ville; Ajdin, Boris; Vaquero, Daniel; Lensch, Hendrik P. A.

doi:10.1145/1833351.1778766

Cited by 4 publications

(2 citation statements)

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“…We use a 3M MP160 projector running at 800×600 at 60Hz and a Nokia N9 camera-phone using the FCam API [Adams et al 2010] generating raw data at 1632×1232 at 13Hz (75ms exposure time). The FCam API makes it easier for us to synchronize the camera with the projector, and the raw stream prevents us from losing resolution to demosaicing.…”

Section: Hardware Setupmentioning

confidence: 99%

Sculpting by numbers

Rivers¹,

Adams²,

Durand³

2012

ACM Trans. Graph.

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a) Target 3D model (b) Guidance projected onto material (c) Sculpted physical replica Figure 1: We assist users in creating physical objects that match digital 3D models. Given a target 3D model (a), we project different forms of guidance onto a work in progress (b) that indicate how it must be deformed to match the target model. As the user follows this guidance, the physical object's shape approaches that of the target (c). With our system, unskilled users are able to produce accurate physical replicas of complex 3D models. Here, we recreate the Stanford bunny model (courtesy of the Stanford Computer Graphics Laboratory) out of polymer clay. AbstractWe propose a method that allows an unskilled user to create an accurate physical replica of a digital 3D model. We use a projector/camera pair to scan a work in progress, and project multiple forms of guidance onto the object itself that indicate which areas need more material, which need less, and where any ridges, valleys or depth discontinuities are. The user adjusts the model using the guidance and iterates, making the shape of the physical object approach that of the target 3D model over time. We show how this approach can be used to create a duplicate of an existing object, by scanning the object and using that scan as the target shape. The user is free to make the reproduction at a different scale and out of different materials: we turn a toy car into cake. We extend the technique to support replicating a sequence of models to create stop-motion video. We demonstrate an end-to-end system in which real-world performance capture data is retargeted to claymation. Our approach allows users to easily and accurately create complex shapes, and naturally supports a large range of materials and model sizes.

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Section: Hardware Setupmentioning

confidence: 99%

Sculpting by numbers

Rivers¹,

Adams²,

Durand³

2012

ACM Trans. Graph.

Self Cite

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“…Such a programmable camera would even provide the possibility to create true high dynamic range images, if the response function could be determined for the integrated camera. However, to the best of our knowledge, the only mobile device capable of controlling camera parameters is the Nokia N900 with Frankencam API [1] . It seems unlikely that fully programmable cameras will become widespread in the foreseeable future.…”

Section: Enhanced Re-detection Of Annotations In Panorama Mapsmentioning

confidence: 99%

Robust detection and tracking of annotations for outdoor augmented reality browsing

Langlotz

Degendorfer

Mulloni

et al. 2011

Computers & Graphics

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A common goal of outdoor augmented reality (AR) is the presentation of annotations that are registered to anchor points in the real world. We present an enhanced approach for registering and tracking such anchor points, which is suitable for current generation mobile phones and can also successfully deal with the wide variety of viewing conditions encountered in real life outdoor use. The approach is based on on-the-fly generation of panoramic images by sweeping the camera over the scene. The panoramas are then used for stable orientation tracking, while the user is performing only rotational movements. This basic approach is improved by several new techniques for the re-detection and tracking of anchor points. For the re-detection, specifically after temporal variations, we first compute a panoramic image with extended dynamic range, which can better represent varying illumination conditions. The panorama is then searched for known anchor points, while orientation tracking continues uninterrupted. We then use information from an internal orientation sensor to prime an active search scheme for the anchor points, which improves matching results. Finally, global consistency is enhanced by statistical estimation of a global rotation that minimizes the overall position error of anchor points when transforming them from the source panorama in which they were created, to the current view represented by a new panorama. Once the anchor points are redetected, we track the user's movement using a novel 3-degree-of-freedom orientation tracking approach that combines vision tracking with the absolute orientation from inertial and magnetic sensors. We tested our system using an AR campus guide as an example application and provide detailed results for our approach using an off-the-shelf smartphone. Results show that the re-detection rate is improved by a factor of 2 compared to previous work and reaches almost 90% for a wide variety of test cases while still keeping the ability to run at interactive frame rates.

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Mobile Computational Photography with FCam

Pulli

Troccoli

2014

Registration and Recognition in Images and Videos

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The Frankencamera

Cited by 4 publications

References 0 publications

Sculpting by numbers

Sculpting by numbers

Robust detection and tracking of annotations for outdoor augmented reality browsing

Mobile Computational Photography with FCam

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