A robust and accurate facial 3D reconstruction method from images acquired by mobile device at home for facial growth monitoring

A robust and accurate facial 3D reconstruction method from images acquired by mobile device at home for facial growth monitoring project at University of Dundeeaims to alleviate the requirement of patients having to visit the clinic by developing a computer vision system to obtain highly accurate and dense facial 3D reconstruction from images acquired with end-user mobile devices in real world.

Context of A robust and accurate facial 3D reconstruction method from images acquired by mobile device at home for facial growth monitoring project at University of Dundee

In Computer Vision, 3D reconstruction of the face from images acquired with uncalibrated cameras is a well-studied problem. Highly accurate 3D models can be obtained from multiple images with optimization-based methods relying on motion analysis and geometrical constraints . Recently, deep learning approaches combining geometry and light properties estimation have emerged. While they achieve impressive results and allow reconstruction from images captured by laypersons, their applicability for anatomical measurement has not been validated. Moreover, these methods do not leverage all the progress made to integrate multi-view geometry constraints in deep learning-based Structure-from-Motion

• In the proposed research, we will build upon these recent works and aim to produce an accurate facial 3D reconstruction method combining the well-established strengths of traditional geometry-based 3D reconstruction with the versatility of deep-learning-based facial reconstruction.
• Another important aspect of the envisaged system will be its robustness to challenging conditions due to the acquisition of the images being done by laypersons at home.