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"3D computer vision" as a book category covers methods and applications for recovering, representing, analyzing, and using three-dimensional information from images and sensor data. Books in this category discuss geometric foundations (projective geometry, multi-view geometry), depth and surface reconstruction, stereo, structure-from-motion, SLAM, depth sensors and LiDAR, pose estimation, 3D shape modelling and segmentation, and modern learning-based approaches (deep networks for depth, 3D CNNs, point clouds). They typically blend theory (optimization, probabilistic inference, calibration) with practical algorithms, evaluation, datasets, and implementation details for applications such as robotics, AR/VR, autonomous driving, photogrammetry, and medical imaging. Readers range from students and researchers seeking mathematical foundations and state-of-the-art methods to engineers looking for applied guidance and code.

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