Geometry and Photometry in 3D Visual Recognition

Shashua, Amnon

doi:10.21236/ada259443

Cited by 161 publications

(93 citation statements)

References 108 publications

(70 reference statements)

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“…One is an intermediate view, between the two original viewing directions, the other is an extrapolation beyond the range spanned by the original views. The gray levels in the combined images were taken as the average of the corresponding points in the original views, for a detailed discussion of combining views containing gray-level information see Shashua (1992).…”

Section: The View-combination Propertymentioning

confidence: 99%

Three-dimensional object recognition based on the combination of views

Ullman

1998

Cognition

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Section: The View-combination Propertymentioning

confidence: 99%

Three-dimensional object recognition based on the combination of views

Ullman

1998

Cognition

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“…It has also been shown that a low dimensional intensity subspace Ð (5-9 dimensions) is sufficient for representing the light variation of most natural scenes [13], [6]. Statistical image variability In a real, imperfectly stabilized image sequence we can expect all of the above types of image variation, as well as unmodeled effects and noise ¡Á .…”

Section: ) Structural Image Variabilitymentioning

confidence: 99%

Predictive display models for tele-manipulation from uncalibrated camera-capture of scene geometry and appearance

Yerex

Cobzaş

Jägersand

2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)

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Abstract-In tele-robotics delayed visual feedback to the human operator can degrade task performance significantly. To improve this, predictive display, uses a scene model to estimate and render immediate visual feedback based on the operator's control commands. Traditional predictive display involves the calibration and overlay of an a-priori model with the delayed real video feedback. In this paper we present an image-based method where the scene geometry and appearance is captured using structure-frommotion by an uncalibrated eye-in-hand camera mounted on the remote robot. The model is then compressed and transmitted to the operator site, where it is used to generate immediate feedback in response to the operators movements. Calibration problems are avoided since the model is captured by the same scene camera as is being simulated in the predictive display. We show experiments where we capture the appearance of a robot hand and transmit it over the network to the operator site where the model renders scene appearance change in response to operator viewpoint motion.

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“…Shashua [6] showed that an image with any light direction can be synthesized by a linear combination of three base images. This method, however, assumes the Lambertian reection model.…”

Section: Photometric Image-based Renderingmentioning

confidence: 99%

“…Taking these factors into account, we formulate an image as follows: the lighting direction. Shashua [6] showed that if a single point light source is assumed at innity, the image with any l i g h ting directions (Î) can be synthesized by a linear combination of three base images (I 1 , I 2 and I 3 ) t a k en in dierent l i g h ting directions, I = a 1 I 1 + a 2 I 2 + a 3 I 3 :…”

Section: The Case Of Real Scenesmentioning

confidence: 99%

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Photometric image-based rendering for virtual lighting image synthesis

Mukaigawa

Mihashi

Shakunaga

Proceedings 2nd IEEE and ACM International Workshop on Augmented Reality (IWAR'99)

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A c oncept named Photometric Image-Based R endering (PIBR) is introduced for a seamless augmented r eality. The PIBR is dened as Image-Based R endering which covers appearance changes caused by the lighting condition changes, while Geometric Image-Based Rendering (GIBR) is dened as Image-Based R endering which covers appearance changes caused by the view point changes. The PIBR can be applied to image synthesis to keep photometric consistency between virtual objects and real scenes in an arbitrary lighting condition. We analyze the conventional IBR algorithms, and formalize the PIBR in the whole IBR framework. A s p ecic algorithm is also presented for realizing the PIBR. The photometric linearization makes a controllable framework for the PIBR, which consists of four processes; (1) separation of environmental illumination eects, (2) estimation of lighting directions, (3) separation of specular reections and cast-shadows, and (4) linearization of self-shadows. After the photometric linearization of input images, we can synthesize any realistic images which include not only diuse reections but also self-shadows, cast-shadows and specular reections. Experimental results show that realistic images can be s u c cessfully synthesized w i t h k e eping photometric consistency.

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Geometry and Photometry in 3D Visual Recognition

Cited by 161 publications

References 108 publications

Three-dimensional object recognition based on the combination of views

Three-dimensional object recognition based on the combination of views

Predictive display models for tele-manipulation from uncalibrated camera-capture of scene geometry and appearance

Photometric image-based rendering for virtual lighting image synthesis

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