Photogrammetric Computer Vision: Statistics, Geometry, Orientation and Reconstruction (Geometry and pdf epub mobi txt 电子书 下载 2026
- 数学-微分几何
- 数学
- Graphics
- 编程
- 三维视觉
- Photogrammetry
- Computer Vision
- 3D Reconstruction
- Geometry
- Statistics
- Orientation
- Image Processing
- Remote Sensing
- Digital Modeling
- Computer Graphics
具体描述
This textbook offers a statistical view on the geometry of multiple view analysis, required for camera calibration and orientation and for geometric scene reconstruction based on geometric image features. The authors have backgrounds in geodesy and also long experience with development and research in computer vision, and this is the first book to present a joint approach from the converging fields of photogrammetry and computer vision.
Part I of the book provides an introduction to estimation theory, covering aspects such as Bayesian estimation, variance components, and sequential estimation, with a focus on the statistically sound diagnostics of estimation results essential in vision metrology. Part II provides tools for 2D and 3D geometric reasoning using projective geometry. This includes oriented projective geometry and tools for statistically optimal estimation and test of geometric entities and transformations and their relations, tools that are useful also in the context of uncertain reasoning in point clouds. Part III is devoted to modelling the geometry of single and multiple cameras, addressing calibration and orientation, including statistical evaluation and reconstruction of corresponding scene features and surfaces based on geometric image features. The authors provide algorithms for various geometric computation problems in vision metrology, together with mathematical justifications and statistical analysis, thus enabling thorough evaluations. The chapters are self-contained with numerous figures and exercises, and they are supported by an appendix that explains the basic mathematical notation and a detailed index.
The book can serve as the basis for undergraduate and graduate courses in photogrammetry, computer vision, and computer graphics. It is also appropriate for researchers, engineers, and software developers in the photogrammetry and GIS industries, particularly those engaged with statistically based geometric computer vision methods.
作者简介
From the Back Cover
This textbook offers a statistical view on the geometry of multiple view analysis, required for camera calibration and orientation and for geometric scene reconstruction based on geometric image features. The authors have backgrounds in geodesy and also long experience with development and research in computer vision, and this is the first book to present a joint approach from the converging fields of photogrammetry and computer vision. Part I of the book provides an introduction to estimation theory, covering aspects such as Bayesian estimation, variance components, and sequential estimation, with a focus on the statistically sound diagnostics of estimation results essential in vision metrology. Part II provides tools for 2D and 3D geometric reasoning using projective geometry. This includes oriented projective geometry and tools for statistically optimal estimation and test of geometric entities and transformations and their relations, tools that are useful also in the context of uncertain reasoning in point clouds. Part III is devoted to modelling the geometry of single and multiple cameras, addressing calibration and orientation, including statistical evaluation and reconstruction of corresponding scene features and surfaces based on geometric image features. The authors provide algorithms for various geometric computation problems in vision metrology, together with mathematical justifications and statistical analysis, thus enabling thorough evaluations. The chapters are self-contained with numerous figures and exercises, and they are supported by an appendix that explains the basic mathematical notation and a detailed index.The book can serve as the basis for undergraduate and graduate courses in photogrammetry, computer vision, and computer graphics. It is also appropriate for researchers, engineers, and software developers in the photogrammetry and GIS industries, particularly those engaged with statistically based geometric computer vision methods.
Read more
About the Author
Prof. Dr.-Ing. Wolfgang Förstner is an internationally leading expert in photogrammetry, computer vision, pattern recognition and machine learning. Throughout his exemplary career of nearly 40 years as a researcher, inventor, innovator and educator, he has made exceptionally significant scientific contributions in many areas of information from imagery and mentored generations of mapping scientists and engineers. Examples of his work include blunder detection for aerial triangulation, image matching, object recognition and statistical projective geometry. He developed the well-known Förstner Operator, for the detection of key points in images, in the 1980s. After studying geodesy and surveying, he first worked at the University of Stuttgart before moving to the University of Bonn as Professor for Photogrammetry where he led the Institute for Photogrammetry from 1990 to 2012. He published more than 100 academic papers, coauthored three book chapters for the ASPRS Manual ofPhotogrammetry, supervised more than 30 Ph.D. theses, and was closely involved with the International Society for Photogrammetry and Remote Sensing and the German Association for Pattern Recognition. Prof. Dr.-Ing. Bernhard P. Wrobel received his Ph.D. (Dr.-Ing) in theoretical geodesy from the University of Bonn. From 1975 to 1981 he was professor for close-range photogrammetryand from 1981 to 2001 for photogrammetry at Darmstadt University of Technology, and also head of the Institute for Photogrammetry and Cartography. He was closely involved with the International Society for Photogrammetry and Remote Sensing, and he coauthored three book chapters for the ASPRS Manual of Photogrammetry. Besides his work related to precise mensuration tasks in industry, his research interests cover the mathematical fundamentals of photogrammetry such as the digital inversion of image formation for reconstruction of 3D surfaces and reflectance from multiple images.Authors' website (code, lecture slides) at http://www.ipb.uni-bonn.de/book-pcv/)
Read more
目录信息
读后感
评分
评分
评分
评分
用户评价
这本书的装帧和纸质手感真是没得挑，拿到手里沉甸甸的，一看就知道是下了血本的硬核教材。书的封面设计很简洁，就是那种典型的学术著作风格，没有太多花哨的东西，专注于内容的呈现。我本来是想找一本关于现代计算机图形学中光线追踪算法与并行化实现方面的内容，希望能深入了解一下实时渲染的底层原理和GPU编程的最新进展。这本书的目录浏览下来，感觉侧重点似乎更偏向于几何重建和运动恢复结构（SfM）这一块，虽然标题里的“Vision”也暗示了图像处理，但我的核心诉求是图形渲染的性能优化和算法细节，而非图像测量学。比如，我很期待看到关于Vulkan或DirectX 12中高级管线状态管理，以及如何高效地利用Compute Shader进行大规模几何体的光照计算的深入讨论，但这本书似乎更侧重于如何从一系列二维图像中精确地构建出三维世界模型，这和我的研究方向——即时渲染场景的生成和显示优化——存在显著的知识鸿沟。不过，作为一本严谨的学术专著，其排版清晰度毋庸置疑，字体选择和图表绘制质量都非常高，即便内容不完全匹配我的需求，从其制作水准来看，它无疑是相关领域内值得收藏的工具书。
评分这本书的语言风格非常典雅，充满了严谨的德式工程哲学，逻辑链条极其缜密，几乎每一个论断都建立在清晰的数学推导之上。这种风格对于追求完美逻辑自洽的读者来说是福音，但对于习惯了更偏向美式工程实用主义和快速迭代思维的工程师来说，阅读体验可能会略显沉重。我购买这本书的初衷是想快速了解当前业界主流的SLAM（同步定位与地图构建）框架，特别是关于回环检测（Loop Closure）中，如何利用诸如DBoW2或更现代的基于深度学习的描述子进行鲁棒性匹配和位姿图优化。这本书显然没有将重点放在这些前沿的、快速迭代的软件工具和库上，而是扎根于经典的几何约束——比如对极几何、本质矩阵、单应性矩阵等，进行极其详尽的代数和几何推导。这种详尽的推导虽然能让你彻底理解为什么这些方法有效，但却耗费了大量时间，让我感觉自己更像是在重温一次严谨的微分几何课程，而不是在学习如何快速部署一个现代的视觉里程计系统。
评分这本书的厚度和内容密度是毋庸置疑的，每一页都塞满了公式和严谨的定义。我购买它是希望找到一套完整的、可操作的指南，用于处理室外大型场景下，无人机获取影像的自动化正射影像（Orthophoto）制作流程，特别是关于如何解决植被阴影、大气透视等复杂光学失真问题的处理流程。我期待看到关于正射纠正中，如何高效地建立和应用高分辨率数字地表模型（DSM）以及如何进行辐射校正的详细工业级流程。但这本书的侧重点，在我看来，似乎更像是一部为构建几何模型本身服务的理论教科书，而非直接面向最终产品（如正射影像或三维城市模型）的生产流程手册。它深入探讨了如何从点云数据中提取精确的几何关系，比如坐标系之间的变换和误差的传播分析，这无疑是核心中的核心。然而，对于那些像我一样，需要快速整合现有工具链（如Pix4D或Metashape）并针对特定传感器（如高分辨率航空相机）进行参数调优的工程人员来说，这本书更像是提供了理解这些工具背后数学原理的终极参考，而不是一步步教你如何操作软件的“用户手册”。它的价值在于理解“为什么”，而不是“怎么做”这个层面的工程实践。
评分从图书馆借阅的体验来看，这本书的引用部分做得非常到位，参考文献列表长得出奇，几乎覆盖了自上世纪七八十年代以来所有里程碑式的论文，这表明作者在资料的搜集和梳理上投入了巨大的心血。我原本希望这本书能提供关于三维重建中，如何利用现代深度学习技术（例如NeRF或隐式神经表示）来提升场景细节的保真度和渲染质量。我关注的重点是，如何将这些基于神经网络的表示与传统的几何模型进行有效的结合，以解决光照变化和遮挡问题。然而，这本书的“Reconstruction”部分，似乎更侧重于传统的结构恢复方法，比如Bundle Adjustment（束优化）的各个变体、最小化能量函数的设计思路等，这些都是基于经典的几何和统计模型。尽管它提供了关于“Orientation”（姿态估计）的坚实基础，但对于那些热衷于探索最新的生成模型和神经渲染技术，期望看到如何用PyTorch或TensorFlow实现这些新范式的读者来说，这本书的侧重点显得有些“复古”或说“经典”——它构建的是知识的基石，而不是快速到达应用尖端的桥梁。
评分我本来是为了解决一个非常具体的传感器融合问题而购入此书，期待它能提供关于卡尔曼滤波（Kalman Filter）或扩展卡尔曼滤波（EKF）在多模态数据（例如激光雷达点云与视觉特征）联合定位中的高级应用和误差建模的详尽论述。我特别想找的是那些能够处理非线性和高斯假设局限性的无迹卡尔曼滤波（UKF）或粒子滤波（PF）在三维空间跟踪中的优化策略。然而，通读了前几章后，我发现本书的“统计”部分似乎更倾向于对观测噪声的几何分布特性进行严谨的数学建模，比如最小二乘优化、最小中位数平方（LMS）估计等，这些固然重要，但对于我当前急需的动态系统状态估计，特别是实时性能要求下的迭代优化方法，介绍得相对保守和理论化。这本书更像是在打地基，提供坚实的数学基础，而不是直接搭建应用的高层框架。如果我需要一本关于如何编写高效的C++库来处理实时传感器数据流并输出精准姿态估计的书，这本书可能需要搭配一本专门的滤波理论书籍一起使用。它对理论的深度挖掘令人印象深刻，但对于追求工程实现效率和快速原型验证的读者来说，可能需要更多的“菜谱”式指导。
评分 评分 评分 评分 评分相关图书
本站所有内容均为互联网搜索引擎提供的公开搜索信息，本站不存储任何数据与内容，任何内容与数据均与本站无关，如有需要请联系相关搜索引擎包括但不限于百度，google,bing,sogou 等
© 2026 book.quotespace.org All Rights Reserved. 小美书屋 版权所有