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"Shape reconstruction" as a book category covers the theory, algorithms, and applications for recovering the geometric form of objects or scenes from incomplete, noisy, or indirect data. It spans methods for turning images, point clouds, depth maps, range scans, or sparse measurements into continuous surfaces or volumetric models—covering topics such as multi-view stereo, photogrammetry, surface and volume meshing, implicit and parametric representations, registration, denoising, topology inference, and recent learning-based (including neural implicit) approaches. Books in this category typically combine foundations in geometry, optimization, and signal processing with practical issues like sensor noise, occlusion, scalability, evaluation metrics, and software implementations, and illustrate use cases in computer vision and graphics, robotics, AR/VR, medical imaging, manufacturing, and cultural heritage. They are aimed at researchers, graduate students, and practitioners seeking both theoretical understanding and hands-on techniques for reconstructing accurate, usable shapes from real-world data.

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