Joint Estimation of Shape and Reflectance using Multiple Images with Known Illumination Conditions

Yoon, Kuk-Jin; Prados, Emmanuel; Sturm, Peter

doi:10.1007/s11263-009-0222-4

Cited by 33 publications

(23 citation statements)

References 46 publications

(75 reference statements)

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“…Also, although the proposed approach can in principle deal with very general scenarios, especially the case of estimating specular coefficients remains challenging in practice due to numerical issues. A discussion of such practical aspects is provided in [9].…”

Section: Resultsmentioning

confidence: 99%

“…The developed framework for optimizing photoconsistency was then used to develop a general purpose algorithm for modeling 3D surface and appearance [8,9]. Here, we considered the case where lighting conditions are known (we modeled this as a set of point or directional light sources, plus an ambient lighting) but may be different for each input image.…”

Section: Overviewmentioning

confidence: 99%

See 1 more Smart Citation

3D and Appearance Modeling from Images

Sturm

Delaunoy

Gargallo

et al. 2009

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. This paper gives an overview of works done in our group on 3D and appearance modeling of objects, from images. The backbone of our approach is to use what we consider as the principled optimization criterion for this problem: to maximize photoconsistency between input images and images rendered from the estimated surface geometry and appearance. In initial works, we have derived a general solution for this, showing how to write the gradient for this cost function (a non-trivial undertaking). In subsequent works, we have applied this solution to various scenarios: recovery of textured or uniform Lambertian or non-Lambertian surfaces, under static or varying illumination and with static or varying viewpoint. Our approach can be applied to these different cases, which is possible since it naturally merges cues that are often considered separately: stereo information, shading, silhouettes. This merge naturally happens as a result of the cost function used: when rendering estimated geometry and appearance (given known lighting conditions), the resulting images automatically contain these cues and their comparison with the input images thus implicitly uses these cues simultaneously. OverviewImage-based 3D and appearance modeling is a vast area of investigation in computer vision and related disciplines. A recent survey of multi-view stereo methods is given in [6]. In this invited paper, we provide a brief overview of a set of works done in our group, mainly by showing sample results. Technical details can be found in the relevant cited publications.3D and appearance modeling from images, like so many estimation problems, is usually formulated, explicitly or implicitly, as a (non-linear) optimization problem 1 . One of the main questions is of course which criterion to optimize. We believe that the natural criterion is to maximize photoconsistency between input images and images rendered from the estimated surface geometry and appearance (to be precise, this criterion corresponds to the likelihood term of a Bayesian problem formulation, which can be combined with suitable priors). To measure 1 There exist some exceptions in special cases. For example, in basic shape-fromsilhouettes, the 3D shape is directly defined by the input and no estimation is necessary, just a computation to explicitly retrieve the shape.E.

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Section: Resultsmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

3D and Appearance Modeling from Images

Sturm

Delaunoy

Gargallo

et al. 2009

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Self Cite

View full text Add to dashboard Cite

show abstract

“…Yoon, Prados, and Sturm show in [14] how they performed three-dimensional reconstruction. Their work is based on multiple photographs of the object at different, known positions.…”

Section: A Three-dimensional Reconstruction Methodsmentioning

confidence: 99%

“…Many papers such as the one written by Yoon et al [14]; Maeda et al [9]; and Beling da Rosa [5] show eye disease detection methods that require the use of external accessories. On contrast, this method only uses the accelerometers, gyroscope, and camera.…”

Section: Introductionmentioning

confidence: 99%

Detecting topographical eye disorders

Patel¹,

Gouia‐Zarrad

2017

BIOMATH

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Abstract-The goal of this paper is to outline a novel method of detecting topographical eye diseases using a mobile phone. Because the shape of the eye is of concern, its exterior will be evaluated. The description and current implementation of the technique will be discussed, where the keratoconus disease is used as an example. The technique uses pictures taken of the eye using the onboard camera, and each one is compared to idealized images rendered in the phone. The pictures taken will be compared to the idealized images to search for the presence of the disease.

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“…In general, most of these methods focus on modeling or applying the sky spatial radiance distributions rather than spectral information. Spectral information, i.e., the SPD knowledge of light sources, is useful in some computer vision tasks, such as reflectance recovery [8], camera spectral sensitivities estimation [9], color constancy [10], relighting [11], image rendering [12], and augmented reality [13].…”

Section: Introductionmentioning

confidence: 99%

Simple and effective calculations about spectral power distributions of outdoor light sources for computer vision

et al. 2016

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Abstract:The Spectral Power Distributions (SPD) of outdoor light sources are not constant over time and atmospheric conditions, which causes the appearance variation of a scene and common natural illumination phenomena, such as twilight, shadow, and haze/fog. Calculating the SPD of outdoor light sources at different time (or zenith angles) and under different atmospheric conditions is of interest to physically-based vision. In this paper, for computer vision and its applications, we propose a feasible, simple, and effective SPD calculating method based on analyzing the transmittance functions of absorption and scattering along the path of solar radiation through the atmosphere in the visible spectrum. Compared with previous SPD calculation methods, our model has less parameters and is accurate enough to be directly applied in computer vision. It can be applied in computer vision tasks including spectral inverse calculation, lighting conversion, and shadowed image processing. The experimental results of the applications demonstrate that our calculation methods have practical values in computer vision. It establishes a bridge between image and physical environmental information, e.g., time, location, and weather conditions.

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Joint Estimation of Shape and Reflectance using Multiple Images with Known Illumination Conditions

Cited by 33 publications

References 46 publications

3D and Appearance Modeling from Images

3D and Appearance Modeling from Images

Detecting topographical eye disorders

Simple and effective calculations about spectral power distributions of outdoor light sources for computer vision

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