Survey on 3D face reconstruction from uncalibrated images
Araceli Morales, Gemma Piella, Federico M. Sukno

TL;DR
This survey reviews recent methods for reconstructing 3D faces from uncalibrated 2D images, highlighting the rise of deep learning approaches and discussing challenges and future directions.
Contribution
It provides a comprehensive classification of 3D face reconstruction techniques based on prior knowledge strategies and analyzes recent trends and publicly available models.
Findings
Deep learning methods are now the dominant approach.
Photometry-based methods have declined due to limitations.
Statistical models remain important as prior knowledge.
Abstract
Recently, a lot of attention has been focused on the incorporation of 3D data into face analysis and its applications. Despite providing a more accurate representation of the face, 3D facial images are more complex to acquire than 2D pictures. As a consequence, great effort has been invested in developing systems that reconstruct 3D faces from an uncalibrated 2D image. However, the 3D-from-2D face reconstruction problem is ill-posed, thus prior knowledge is needed to restrict the solutions space. In this work, we review 3D face reconstruction methods proposed in the last decade, focusing on those that only use 2D pictures captured under uncontrolled conditions. We present a classification of the proposed methods based on the technique used to add prior knowledge, considering three main strategies, namely, statistical model fitting, photometry, and deep learning, and reviewing each of…
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