2001
DOI: 10.1007/s003710100116
|View full text |Cite
|
Sign up to set email alerts
|

A spectrally based framework for realistic image synthesis

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
46
0

Year Published

2006
2006
2013
2013

Publication Types

Select...
3
3
1

Relationship

0
7

Authors

Journals

citations
Cited by 46 publications
(46 citation statements)
references
References 42 publications
0
46
0
Order By: Relevance
“…We assume only non dispersive objects, thus allowing a vector representation of the spectral interaction between the light and the surface. Dispersive objects should be handled by a specific data structure such as the one proposed by Sun [67]. First, because we want to test our algorithms with real measurements that are currently very difficult to obtain for BTDF.…”
Section: Radiometrymentioning
confidence: 99%
See 2 more Smart Citations
“…We assume only non dispersive objects, thus allowing a vector representation of the spectral interaction between the light and the surface. Dispersive objects should be handled by a specific data structure such as the one proposed by Sun [67]. First, because we want to test our algorithms with real measurements that are currently very difficult to obtain for BTDF.…”
Section: Radiometrymentioning
confidence: 99%
“…Spectral representations rely on polynomials [55], basis functions [16,52], or hybrid schemes [67]. Relevant wavelength selection in the context of spectral rendering has also been studied [76,42].…”
Section: Separable Decompositionsmentioning
confidence: 99%
See 1 more Smart Citation
“…This area has widespread applications in industry and scientific research including 3D design, computer animation, scientific visualization and virtual reality. In the last twenty-five years, a large number of research papers have been devoted to this area [3]. Nowadays, as people are enjoying the pleasure of realistically duplicating objects and complex scenes in the virtual world, synthesis of the human face has become one of the most promising research fields, because the face appears as the most perceptual knowledge of human beings [2].…”
Section: Introductionmentioning
confidence: 99%
“…These methods can be classified into three categories: anatomy based methods, geometry based methods and learning methods. Anatomy based methods build face models by estimating the dynamic facial muscle contractions from a sequence of human face images [3]. It needs a lot of pre-processing, such as registration of corresponding muscle points and setting constraints of muscle contraction to every sample.…”
Section: Introductionmentioning
confidence: 99%