入门级图像合成教程：RenderMan渲染技术详解

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"Focal.Press: Rendering for Beginners - Image Synthesis using RenderMan (2005)" 这本书籍是一本由Saty Raghavachary编著的专业IT教程，专注于渲染技术入门，特别是使用RenderMan进行图像合成。RenderMan是一种高级的图形渲染软件，常用于电影、电视和游戏等领域的专业视觉效果制作。该书面向的是初学者，旨在提供一个清晰易懂的指南，帮助读者掌握渲染的基本概念和技术。在《Focal Press》中，作者详细讲解了如何利用RenderMan创建逼真的图像，包括光照模型、纹理处理、阴影和反射等方面的知识。书中可能涵盖了渲染流程，从场景设置到最终输出的整个过程，包括但不限于以下关键知识点： 1. **渲染基础**：介绍渲染的概念，解释什么是光线追踪、全局光照、局部光照模型以及它们在图像合成中的作用。 2. **RenderMan工具介绍**：讲解RenderMan的核心功能，如Shading Language (SL)语言，用户界面，以及如何编写渲染节点来控制光照和材质。 3. **光照与材质**：讲解不同类型的光源（如点光源、聚光灯、平行光）及其参数调整，以及如何使用各种材质系统实现真实感的表面表现。 4. **纹理处理**：涵盖纹理映射、环境贴图、位图纹理和高级纹理技术，如MIPmapping和LOD（Level of Detail）。 5. **阴影与反射**：讨论阴影的类型（硬阴影、软阴影）、全局照明中的间接照明，以及如何模拟镜面反射和折射。 6. **性能优化**：介绍如何提高渲染效率，包括设置合理的渲染参数、使用渲染队列和渲染层级，以及对硬件的充分利用。 7. **案例研究与实战项目**：书中可能包含一系列实际案例，让读者通过实践加深理解，并逐步提升技能。 8. **版权和许可**：最后部分强调版权法规定，确保读者在使用书中内容时遵循正确的授权条款。这本著作对于想要进入渲染领域或提升现有技能的初学者来说，是一本不可或缺的参考资料，提供了深入浅出的指导，使他们在实践中掌握渲染技术的核心原理。

Figure 1.2 Three routes to image synthesis

Think of how you as an artist would render a scene in front of you. Imagine that you would

like to paint a pretty landscape, using oil on canvas. You intuitively form a scene description

of the things that you are looking at, and use creativity, judgment and technique to paint

what you want to portray onto the flat surface. You are the renderer that takes the scene

description and eventually turns it into an image. Depending on your style, you might make

a fairly photorealistic portrait which might make viewers feel as if they are there with you

looking at the landscape. At the other extreme you might produce a very abstract image,

using elements from the landscape merely as a guide to create your own shapes, colors and

placement on canvas. Sorry if I make the artistic process seem mechanical – it does help

serve as an analogy to a CG renderer.

A photographer likewise uses a camera to create flat imagery. The camera acts as the

renderer, and the photographer creates a scene description for it by choosing composition,

lighting and viewpoint.

On a movie set, the classic “Lights, camera, action!” call gets the movie camera to start

recording a scene, set up in accordance with a shooting script. The script is interpreted by

the movie’s Director, who dictates the choice and placement of lights, camera(s) and

actors/props in the scene. As the actors “animate” while delivering dialog, the movie camera

renders the resulting scene to motion picture film or digital output media. The Director sets

up the scene description and the camera renders it.

In all these cases, scene descriptions get turned into imagery. This is just what a CG

renderer does. The scene is purely synthetic, in the sense that it exists only inside the

machine. The renderer’s output (rendered image) is equally synthetic, being a collection of

- form

- space

- light

- lights

- camera

- action!

- geometry

- materials

- lights

- camera

fine artist

film director

artist

images

on canvas

images

via

movie camera

images

using a

renderer

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Figure 1.4 RenderMan converts RIB inputs into images

You can see that the RIB file contains concrete specifications for what we want RenderMan

to do. For example “Color [1 0 0]” specifies red color for the sphere (in RGB color space).

The RIB file shown produces the image shown. If we made derivative versions of RIB files

from the one above (e.g. by changing the “Translate 0 0 15” to “Translate 0 0 18”, then to

“Translate 0 0 21” and so on, which would pull the camera back from the scene each step,

and by changing the “pln_sph.tiff” to “pln_sph2.tiff”, then to “pln_sph3.tiff”, etc. to specify a

new image file name each time), RenderMan will be able to read each RIB file and convert

it to an image named in that RIB file. When we play back the images rapidly, we will see an

animation of the scene where the light and two objects are static, and the camera is being

pulled back (as in a dolly move – see Chapter 6, “Camera, output”). The point is that a

movie camera takes near-continuous snapshots (at 24 frames-per-second, 30 frames-per-

second, etc.) of the continuous scene it views, while a CG renderer is presented scene

snapshots in the form of a scene description file, one file per frame of rendered animation.

Persistence of vision in our brains is what causes the illusion of movement in both cases,

when we play back the movie camera’s output as well as a CG renderer’s output.

1.3 Representational styles

With the eye/camera/CG renderer analogy in mind, it is time to look at the different ways

that renderers can render scene descriptions for us.

For the most part, we humans visually interpret the physical world in front of us fairly

identically. The same is generally true for cameras, aside from differences in lenses and

film/sensor type. Their inputs come from the real world, get processed through optical

elements based on physical and geometric laws, leading to image formation on physical

media. But this is not how CG renderers work. As you know by now, their inputs are scene

descriptions. They turn these scene descriptions into imagery, via calculations embodied in

rendering algorithms (recipes or procedures) for image synthesis. The output images are

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really grids of numbers that represent colors. Of course we eventually have to view the

outputs on physical devices such as monitors, printers and film-recorders.

Because rendered images are calculated, depending on the calculations, the same input

scene description can result in a variety of output representations from the renderer. Each

has its use. We will now take a look at several of the most common rendering styles in use.

Each shows a different way to represent a 3D surface. By 3D we do not mean stereo-

viewing, rather we mean that such a surface would exist as an object in the real world,

something you can hold in your hands, walk around, see it be obscured by other objects.

Figure 1.5 Point-cloud representation of a 3D surface

Figure 1.5 shows a point-cloud representation of a torus. Here, the image is made up of just

the vertices of the polygonal mesh that makes up the torus (or of the control vertices, in the

case of a patch-based torus). We will explore polygonal meshes and patch surfaces in detail,

in Chapter 4. The idea here is that we infer the shape of a 3D object by mentally connecting

the dots in its point cloud image. Our brains create in our mind’s eye, the surfaces on which

the dots lie. In terms of Gestalt theories, the law of continuation (where objects arranged in

straight lines or curves are perceived as a unit) and the principle of closure (where groups of

objects complete a pattern) are at work during the mental image formation process.

Next is a wireframe representation, shown in Figure 1.6. As the name implies, this type of

image shows the scaffolding wires that might be used to fashion an object while creating a

sculpture of it. While the torus is easy to make out (due to its simplicity of shape and

sparseness of the wires), note that the eagle mesh is too complex for a small image in

wireframe mode. Wireframe images are rather easy for the renderer to create, in

comparison with the richer representations that follow. In wireframe mode the renderer is

able to keep up with scene changes in real time, if the CG camera moves around an object

or if the object is translated/rotated/scaled. The wireframe style is hence a common preview

mode when a scene is being set up for full-blown (more complex) rendering later.

Figure 1.6 Wireframe view

Rendering for Beginners

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