Appearance sampling for obtaining a set of basis images for variable illumination

Okabe,; Satô,; Ikeuchi,

doi:10.1109/iccv.2003.1238430

Cited by 21 publications

(8 citation statements)

References 18 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While similar is spirit, these approaches measure different physical properties, and are therefore both mathematically and optically very different from ours. Our proposed zonal basis illumination is very similar in principle to harmonic lights as proposed by Sato et al (2003) for encoding the appearance function of a scene. Hence our optical setup can be seen as a step towards the realization of such harmonic lights.…”

Section: Related Workmentioning

confidence: 97%

A Basis Illumination Approach to BRDF Measurement

et al. 2008

View full text Add to dashboard Cite

Realistic descriptions of surface reflectance have long been a topic of interest in both computer vision and computer graphics research. In this paper, we describe a novel high speed approach for the acquisition of bidirectional reflectance distribution functions (BRDFs). We develop a new theory for directly measuring BRDFs in a basis representation by projecting incident light as a sequence of basis functions from a spherical zone of directions. We derive an orthonormal basis over spherical zones that is ideally suited for this task. BRDF values outside the zonal directions are extrapolated by re-projecting the zonal measurements into a spherical harmonics basis, or by fitting analytical reflection models to the data. For specular materials, we experiment with alternative basis acquisition approaches such as compressive sensing with a random subset of the higher order orthonormal zonal basis functions, as well as measuring the response to basis defined by an analytical model as a way of optically fitting the BRDF to such a representation. We verify this approach with a compact optical setup that requires no moving parts and only a small number of image measurements. Using this approach, a BRDF can be measured in just a few minutes.A. Ghosh ( ) ·

show abstract

Section: Related Workmentioning

confidence: 97%

A Basis Illumination Approach to BRDF Measurement

et al. 2008

View full text Add to dashboard Cite

show abstract

“…These representations provide a means of interpolating between samples and storing surface information in a concise format. Representations methods that have been employed thus far for BTF's include principle components analysis (PCA) [42,59,39,19], spherical harmonics [47], basis functions of recovered BRDF's [27], tensor factorization [54], and steerable basis textures [2].…”

Section: Recent Developments In Btf Measurements and Modelsmentioning

confidence: 99%

Texture for Appearance Models in Computer Vision and Graphics

Cula¹,

Dana²

2008

Handbook of Texture Analysis

View full text Add to dashboard Cite

Appearance modeling is fundamental to the goals of computer vision and computer graphics. Traditionally, appearance was modeled with simple shading models (e.g. Lambertian or specular) applied to known or estimated surface geometry. However, real world surfaces such as hair, skin, fur, gravel, scratched or weathered surfaces, are difficult to model with this approach for a variety of reasons. In some cases it's not practical to obtain geometry because the variation is so complex and fine-scale. The geometric detail is not resolved with laser scanning devices or with stereo vision. Simple reflectance models assume that all light is reflected from the point where it hits the surface, i.e. no light is transmitted into the surface. But in many real surfaces, a portion of the light incident on one surface point is scattered beneath the surface and exits at other surface points. This subsurface scattering causes difficulties in accurately modeling a surface such as frosted glass or skin with a simple geometry plus shading model. So even when a precise geometric profile is attainable, applying a pointwise shading model is not sufficient. Because of these issues, image-based modeling has become a popular alternative to modeling with geometry and point-wise shading.Real world surfaces are often textured with a variation in color (as in a paisley print or leopard spots) or a fine-scale surface height variation (e.g. crumpled paper, rough plaster, sand). Surface texture complicates appearance prediction because local shading, shadowing, foreshortening and occlusions change the observed appearance when lighting or viewing directions and changed. As an example, consider a globally planar surface of wrinkled leather where large local shadows appear when the surface is obliquely illuminated and disappear when the surface is frontally illuminated. Accounting for the variation of appearance due to changes in imaging parameters is a key issue in developing accurate models. The terms BRDF and BTF have been used to describe surface appearance. The BRDF (bidirectional reflectance distribution function) describes surface reflectance as a function of viewing and illumination angles. Since surface reflectance varies spatially for textured surfaces, the BTF was introduced to add a spatial variation. More specifically, the bidirectional texture function (BTF) is observed image texture as a function of viewing and illumination directions. In this chapter, topics in BRDF and BTF modeling for vision and graphics are presented. Two methods for recognition are described in detail: (1) bidirectional feature histograms and (2) symbolic primitives that are more useful for recognizing subtle differences in texture.

show abstract

“…On the other hand, resolving surface reflections is still challenging. Sato et al proposed a method to calculate the bidirectional reflectance distribution function (BRDF) as spherical harmonic coefficients using object appearance sampling based on point light sources [10]. This method captured 36 images by changing light source positions and estimated basis images corresponding to the low-order coefficients of spherical harmonic functions.…”

Section: Introductionmentioning

confidence: 99%

Location Dependent Illumination Modeling for Multiple-Camera-based 3D Object Reconstruction

Utsumi¹,

Yamazoe²,

Abe³

2007

17th International Conference on Artificial Reality and Telexistence (ICAT 2007)

View full text Add to dashboard Cite

We propose a method for modeling ununiform illumination conditions using multiple-camera-based marker observations. In computer graphics applications, multiplecamera-based object reconstruction is becoming popular for modeling 3D objects. However, geometrical and photometrical calibrations among multiple cameras still require high computational cost. We employ multiple observations of a spherical marker in a sequence for modeling illuminations. These data are also used for calibrating camera geometries based on a bundled adjustment method to minimize re-projection errors. The illumination condition is modeled as a set of luminance patterns on the virtual planes surrounding the target observation area. An illumination model at an arbitrary position in the scene can be constructed by interpolating the luminance behavior of surrounding planes. Experimental results show the effectiveness of our proposed method.

show abstract

Appearance sampling for obtaining a set of basis images for variable illumination

Abstract: Abstract

Cited by 21 publications

References 18 publications

A Basis Illumination Approach to BRDF Measurement

A Basis Illumination Approach to BRDF Measurement

Texture for Appearance Models in Computer Vision and Graphics

Location Dependent Illumination Modeling for Multiple-Camera-based 3D Object Reconstruction

Contact Info

Product

Resources

About