Craniofacial Point Cloud Generation Model Based on Deep Learning
Aiming at the problems that the existing craniofacial restoration methods have high requirements on the integrity of the initial skull data,long reconstruction and restoration time,and poor scalability and practicability in practical applications,this paper designs and proposes a shape inpainting and shape-learning craniofacial point cloud restoration model based on deep learn-ing.The model uses the rAE to restore the complete skull point cloud from the missing skull point cloud data,and uses the gAE to generate the head and face point cloud corresponding to the original skull.Experiments show that the restoration accuracy of the mod-el complementing the generated skull point cloud and the learned head point cloud can well support the 3D reconstruction of the head model.Compared with traditional methods,the deep learning method proposed in this paper can quickly,realistically and ac-curately reconstruct the three-dimensional head shape from incomplete skull data.It shows good stability and scalability in classical craniofacial restoration tasks,such as the completion of unearthed incomplete ancient human skulls,face restoration,and criminal case detection.
万方数据
维普
