3D Vision: From Geometric Models to Deep Networks – Hardcover

The field of 3D vision is undergoing a major transformation, driven by the convergence of geometric modeling and deep learning. Classical geometric methods have long been central to 3D reconstruction, scene understanding, and object recognition, while data-driven approaches now open new pathways for tackling complex real-world challenges. Yet this shift has created a fragmented landscape where geometric rigor and learning-based flexibility often remain disconnected.

This academic monograph bridges that gap by offering a comprehensive and unified perspective on the evolution of 3D vision. It brings together diverse contributions from researchers and practitioners, presenting a curated selection of works that highlight innovation across theory, methodology, and application. Designed as both a scientific reference and a pedagogical resource, the volume provides structured overviews, case studies, benchmarks, and insights into practical implementations.

Intended for researchers, PhD students, engineers, and educators in computer vision, robotics, and artificial intelligence, the book combines theoretical depth with empirical results to document the paradigm shift in 3D vision. More than a collection of papers, it offers a cohesive narrative of how the field is evolving, equipping readers with tools, knowledge, and inspiration to engage with the future of 3D vision.