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Deformable Surface 3D Reconstruction from Monocular Images (Synthesis Lectures on Computer Vision)

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Deformable surface 3D reconstruction from monocular images is an active area of research in the Computer Vision community. This encompasses recovering both the shape of thin objects that can be treated as surfaces without perceptible thickness and the visible envelope of fully 3D objects. Whereas this may seem easy for a human being, it remains a challenging and ambiguous problem for computer-based techniques.This is especially true when the sensor data is noisy, which is typically the case when dealing with real images.

Being able to recover the shape of 3D deformable surfaces from a single video stream would make it possible to field reconstruction systems that run on widely available hardware without requiring specialized devices. However, because many different 3D shapes can have virtually the same projection, such monocular shape recovery is inherently ambiguous. In this survey, we will review the two main classes of techniques that have proved most effective so far: The template-based methods that rely on establishing correspondences with a reference image in which the shape is already known, and non-rigid structure-from-motion techniques that exploit points tracked across the sequences to reconstruct a completely unknown shape. In both cases, we will formalize the approach, discuss its inherent ambiguities, and present the practical solutions that have been proposed to resolve them. To conclude, we will suggest directions for future research. Table of Contents: Introduction / Early Approaches to Non-Rigid Reconstruction / Formalizing Template-Based Reconstruction / Performing Template-Based Reconstruction / Formalizing Non-Rigid Structure from Motion / Performing Non-Rigid Structure from Motion / Future Directions

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