3D Computer Vision

3D Computer Vision by Yu-Jin Zhang is a definitive, modern guide to understanding how machines perceive depth, shape, and motion in the real world. Combining rigorous theory with clear, real-world examples, this book cuts through complexity to deliver practical insights for researchers, graduate students, and engineers working in robotics, AR/VR, autonomous vehicles, medical imaging, remote sensing, and mapping.

Begin with core principles of multi-view geometry and stereo vision, then progress to advanced topics such as structure-from-motion, dense reconstruction, point-cloud processing, SLAM, and deep learning approaches for 3D perception. Zhang balances mathematical clarity with intuitive explanations, illustrated algorithms, and visual examples that make difficult concepts accessible without sacrificing precision.

Readers will appreciate the emphasis on problem-solving: comparative analyses of algorithms, trade-offs in sensor fusion, and guidance on selecting methods for specific applications and environments. The book’s international perspective makes it relevant for technical teams in North America, Europe, Asia-Pacific, and research labs worldwide, with examples that reflect diverse datasets and deployment scenarios.

Whether you’re building robot navigation systems, improving 3D scanning pipelines, or advancing research in computer vision, this volume equips you with the foundational knowledge and actionable insights to design robust, scalable solutions. Clear chapter summaries and targeted further-reading recommendations accelerate learning and reference.

For professionals and students seeking a single, authoritative resource on 3D computer vision, 3D Computer Vision by Yu-Jin Zhang is an essential addition to technical libraries and academic coursework. Add it to your collection and advance your mastery of three-dimensional perception today.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.