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Depth estimation as a book category covers methods and theory for inferring three-dimensional structure from visual and sensor data, bringing together computer vision, robotics, photogrammetry and machine learning. Titles in this category typically explain core techniques—stereo and multi-view matching, monocular and deep learning-based depth prediction, structure-from-motion, LiDAR and time-of-flight sensing, sensor fusion and SLAM—along with evaluation, datasets and practical considerations like calibration and noise handling. Such books range from introductory surveys and mathematical treatments of geometry and optimization to hands-on guides for implementing algorithms, training neural networks, and deploying systems for autonomous vehicles, AR/VR, robotics, 3D mapping and medical imaging. Readers are usually students, researchers and engineers seeking a mix of theoretical foundations, algorithmic detail and application-driven case studies.

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