计算机图形学

Name: 计算机图形学
Availability: InStock
Rating: 8.5 (67 reviews)
ISBN: 9787115158673

[美]PeterShirley

出版社

人民邮电出版社

出版时间

2007-05-31

ISBN

9787115158673

评分

★★★★★

标签

编程

书籍介绍

这不是一本让人一口气读完的书，却是一本让人愿意反复翻开的书。它最鲜明的特点，是把整个图形学建立在数学之上：坐标系的管理、正交基的维护、矩阵变换的几何含义，都被讲得清清楚楚。读者最常提到的，正是这种“扎实”——它不急着让你写出炫技的渲染器，而是先把旋转、缩放、特征值分解背后的几何直觉立住，让你明白对称矩阵本质上就是一种沿特定方向的缩放。语言精简到近乎冷酷，也因此被不少读者抱怨“枯燥”，翻译更是饱受诟病。但它被国外众多高校选作教材，靠的正是这份不绕弯子的系统感。适合愿意啃数学、追求知其所以然的读者；若只想速成特效，或许会感到吃力。

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。

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

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

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. "