![计算机图形学 - [美]PeterShirley](https://static.book345.com/covers/9787115158673.jpg)
书籍介绍
本书是国外高校采用率很高的计算机图形学教材，共分为26章，全面系统地讲解了计算机图形学的基本概念和相关技术。书中先介绍图形学相关的数学知识，然后依次讲解图形学的光栅算法、三三维观察、隐藏面消除、光照、纹理、绘制等算法和理论，并介绍可视感知、计算机动画、基于图像的绘制、可视化以及构建交互式图形应用等。
本书可作为信息技术等相关专业本科生、研究生计算机图形学课程的教材，也可以作为计算机图形学工作者的参考用书。
AI导读
核心看点
- 系统讲解光栅算法、隐藏面消除等核心概念
- 深入剖析线性代数在图形变换中的底层原理
- 涵盖光线跟踪、纹理映射及交互式应用构建
适合谁读
- 计算机相关专业本科生及研究生
- 从事游戏开发与图形引擎研发的技术人员
- 希望夯实图形学数学基础与理论体系的读者
读前提醒
- 数学推导步骤跳跃，需自备草稿纸自行推算
- 翻译质量一般，建议配合英文原版对照阅读
- 内容偏重纯理论，建议搭配OpenGL红宝书实战
读者共识
- 国外高校经典教材，理论体系扎实且全面
- 数学要求较高，基础薄弱者阅读体验较痛苦
- 虽版本稍旧但原理经典，适合反复查阅参考
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。
目录
用户评论
课本
语言真精简....
还没看完就让小偷偷走了010....
读过最赞的一本 graphcis 的图书，Shirley 多牛逼就不说了，建议看看英文4th 版本
断断续续地读着呢。我又囫囵吞枣啦。。。
基础不好，没看完
教科书，基本没看…
踩踩
很系统
很扎实
深入浅出！相当不错
2版。
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