Metrically-accurate 3D Face Shape Estimation for Applications in Health Care

该项目旨在开发从未校准的商品相机进行度量准确的3D人脸重建的技术。现有的计算机视觉方法，如多视图立体和3D变形模型，将与深度学习的最新进展相结合，以实现这一目标。这填补了现有解决方案之间的空白-它们介于不准确的单图像重建和昂贵的专业性能捕获之间。这两个极端都受到它们在不受约束的环境中的性能的限制。初步结果表明，深度学习技术在这一领域表现良好。该项目将建立在现有工作的基础上，以实现廉价，准确的重建，在不受约束的情况下表现良好。这些技术的应用范围从增加VR的沉浸感到个性化的植入物设计。该项目将侧重于医疗应用，特别是在针灸方面的应用。这充分利用了该部门与利兹大学牙科学院之间的紧密联系。拟议的方法将允许更广泛的人群获得更便宜的护理，改善临床结果。该项目补充了该部现有的研究，并有可能大大提高面部重建的成本和准确性，以广泛应用。

This project aims to develop techniques for metrically-accurate 3D face reconstruction from uncalibrated, commodity cameras. Existing computer vision approaches such as multi-view stereo and 3D morphable models will be combined with the latest advances in deep learning to achieve this aim. This fills a gap between existing solutions - they fall between the extremes of inaccurate single-image reconstruction and expensive professional performance capture. Both extremes are limited by their performance in an unconstrained setting. Initial results have shown great promise for deep learning techniques to perform well in this domain. This project would build upon existing work to allow for inexpensive, accurate reconstruction which performs well in an unconstrained setting.The applications of such techniques range from increasing immersion in VR to personalised implant design. The project will focus on medical applications, particularly those in orthodontics. This takes advantage of the strong connections between the Department and the School of Dentistry at the University of Leeds. The proposed approach will allow for cheaper care which is accessible to a wider group of people, improving clinical outcomes. The project complements existing research at the Department and has the potential to greatly improve the cost and accuracy of facial reconstruction for a broad range of applications.