Free PDF Ebooks Files @AcrobatPlanet.Com

Lighting has a crucial impact on the appearance of 3D objects and on the ability of an image to communicate information about a 3D scene to a human observer. This work presents a new automatic lighting design approach for comprehensible rendering of 3D objects. Given a geometric model of a 3D object or scene, the material properties of the surfaces in the model, and the desired viewing parameters, our approach automatically determines the values of various lighting parameters by optimizing a perception-based image quality objective function. This objective function is designed to quantify the extent to which an image of a 3D scene succeeds in communicating scene information, such as the 3D shapes of the objects, fine geometric details, and the spatial relationships between the objects. Our results demonstrate that the proposed approach is an effective lighting design tool, suitable for users without expertise or knowledge in visual perception or in lighting design.

Lighting design for image synthesis involves specifying values for lighting parameters, such as position, color, and intensity, for each of the light sources in a 3D scene model. Once the scene geometry, the material properties, and the viewing parameters have been specified, the appearance of the scene in a rendered image depends exclusively on the lighting. Poorly designed lighting may result in incomprehensible images, containing under- and over-illuminated regions, exhibiting poor contrast, and failing to effectively communicate the three-dimensional structure of the scene to a human observer.

In order to find an image with a desired appearance, one has to search through the space of possible lighting specifications. The traditional approach towards lighting design for image synthesis typically uses a direct design paradigm, where the user iteratively specifies all of the required lighting parameters, renders the scene, evaluates the results, makes modifications in the design, and so forth. This is essentially a trial-and-error approach, with the obvious drawback that the user must actively participate in each iteration. Thus, the design process is time-consuming and tedious. Furthermore, since the user may need to manipulate several lighting parameters and predict how their values will affect the the resulting image, the process requires expertise in lighting design as well as an understanding of visual perception issues.

An alternative approach is based on an inverse design paradigm. The user is presented with some interface that enables him to specify a set of objectives and/or constraints that the lighting design should satisfy, and the parameters are then solved for in an automatic fashion [6, 14, 23, 30]. These methods, reviewed in Section 2, are certainly less tedious, but still require users to know and to be able to articulate a priori what is the appearance that they desire to achieve. Thus, this approach might still be difficult to use for a non-expert user, whose goal is merely to render a comprehensible image of the scene at hand. There seems to be a need in helping a user to define his lighting design goals. That is, supply some directives for selecting the desired image out of the range of possible images that can be rendered for the scene.

This work presents a novel fully automatic approach to lighting design, geared towards generation of comprehensible, communicative images of 3D objects. More specifically, given a geometric model of a 3D object or scene, the material properties of the surfaces in the model, and the desired viewing parameters, our approach automatically determines the values of various lighting parameters. This is done by optimizing a perception-based image quality objective function designed to quantify the extent to which an image of a 3D scene succeeds in communicating scene information, such as the 3D shape of each object, fine geometric details, and the spatial relationships between the objects in the scene.

Contents
1 Introduction
2 Related Work
3 Visual Perception

3.1 The human visual system
3.2 Psychophysic and Vision research
3.3 Edges
3.4 Derivation of shape information
3.4.1 Depth Cues
3.4.2 Shape From Shading
3.4.3 Shadows

3.5 Other perceptual issues
3.6 Conclusions

4 Perceptual Image Quality Function: Principles

4.1 Target terms
4.1.1 The shading gradients term
4.1.2 The detected edges term
4.1.3 The variance term
4.1.4 The mean term
4.1.5 The histogram equalization term
4.1.6 Light direction term

4.2 Integration of the target terms
4.3 Spatial frequency and color domains

5 Perceptual Image Quality Function: Practice

5.1 Precise definition of the target terms
5.1.1 The detected edges term
5.1.2 The shading gradient term
5.1.3 The variance term
5.1.4 The mean term
5.1.5 The histogram equalization term
5.1.6 The light direction term

5.2 Quality function evaluation procedure

6 Lighting Design

6.1 Methodology
6.2 Process framework
6.3 Implementation
6.3.1 Scene and illumination model specifications
6.3.2 Reducing the number of free parameters
6.3.3 Preliminary geometric computations
6.3.4 Initializing free variables
6.3.5 Constant initializations
6.3.6 Quality function calibration
6.3.7 Optimization
6.3.8 Using the? component
6.3.9 Full automation

6.4 Multiresolution

7 Results
8 Summary and Future work

Download
PDF Ebook: Automatic Lighting Design using a Perceptual Quality Metric