![计算机图形学 - [美]Steve Cunningham](https://static.book345.com/covers/s/9787111241027.jpg)
书籍介绍
《计算机图形学》与大多数传统的计算机图形学教材不同，它仅简要介绍交互式计算机图形学方面的基本知识，主要侧重于介绍计算机图形学在数学及其他科学领域的应用，解决实际问题。《计算机图形学》按照计算机图形学的传统顺序介绍视觉交流、视图变换和投影处理、建模、绘制、光照、着色处理，以及OpenGL API如何实现基本概念和技术，使学生理解并学会使用图形API实现图形操作，为观察者创造有效的图像。
AI导读
核心看点
- 系统讲解光栅、光照、纹理等图形学核心算法
- 从数学基础切入，深入剖析底层实现原理
- 涵盖三维观察、隐藏面消除及交互式应用构建
适合谁读
- 计算机及相关专业的本科生与研究生
- 从事计算机图形学研究与开发的工程师
- 希望深入理解图形学底层逻辑的进阶者
读前提醒
- 数学推导步骤跳跃，需具备扎实线性代数基础
- 理论性强无具体API，建议搭配实践教程阅读
- 翻译质量一般，有条件建议对照英文原版学习
读者共识
- 内容扎实系统，是高校广泛采用的经典教材
- 入门难度较高，新手需耐心啃读数学推导部分
- 常作为图形学入门首选，配合红宝书效果佳
本导读基于书籍简介、目录、原文摘录、短评和书评生成，不等同于全文精读。
精彩摘录
- "The principal difference is between a single rotation and two different orthogonal matrices. This difference causes another, less important, difference. Because the SVD has different singular vectors on the two sides, there is no need for negative Singular values: we can always flip the sign of a si"
- "However, this type of transformation, in which one of the coordinates of the input vector appears in the denominator, can’t be achieved using affine transformations. We can allow for division with a simple generalization of the mechanism of homogeneous coordinates that we have been using for affine "
- "Managing coordinate systems is one of the core tasks of almost any graphics program; key to this is managing orthonormal bases."
- "The advantages of parallel projection are also its limitations. In our everyday experience (and even more so in photographs) objects look smaller as they get farther away, and as a result parallel lines receding into the distance do not ap- pear parallel. This is because eyes and cameras don’t colle"
- "1.Rotate v_1 and v_2 to the x- and y-axes (the transform by R^T). 2.Scale in x and y by (λ_1,λ_2)(the transform by S). 3.Rotate the x- and y-axes back to v_1 and v_2 (the transform by R). Looking at the effect of these three transforms together, we can see that they have the effect of a nonuniform s"
- "If you like to count dimensions: a symmetric 2×2 matrix has 3° of freedom, and the eigenvalue decomposition rewrites them as a rotation angle and two scale factors."
- "A very similar kind of decomposition can be done with non symmetric matrices as well: it's the singular value decomposition(SVD), also discussed in section 6.4.1. The difference is that the matrices on either side of the dialogue matrix are no longer the same: A=USV^T The two orthogonal matrices tha"
- "In summary, every matrix can be decomposed via SVD into a rotation times a scale times another rotation. Only symmetric matrices can be decomposed via eigenvalue diagonalization into a rotation times a scale times the inverse-rotation, and such matrices are a simple scale in an arbitrary direction. "
作者简介
舍利，计算机图形学领域世界知名的学者，尤以光线跟踪方面的研究闻名世界，曾担任ACM Transactions on Graaphics和Jouranl of Graphics Tools副主编，多次担任sIGGRAPH程序委员会委员。他是犹他大学计算机科学系教授。在伊利诺伊大学厄巴纳·尚佩恩分校获得计算机科学博士学位。除本书之外，他还著有Realistic Ray Tracing。
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