An End-to-End Conditional Generative Adversarial Network Based on Depth Map for 3D Craniofacial Reconstruction
An End-to-End Conditional Generative Adversarial Network Based on Depth Map for 3D Craniofacial Reconstruction
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DOI：
10.1145/3503161.3548254
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Niankai Zhang;Junli Zhao;Fuqing Duan;Zhenkuan Pan;Zhongke Wu;Mingquan Zhou;Xianfeng Gu
中科院分区：
文献类型：
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作者：
Niankai Zhang;Junli Zhao;Fuqing Duan;Zhenkuan Pan;Zhongke Wu;Mingquan Zhou;Xianfeng Gu
颅面重建是解决法医案件的基础。由于颅面模型的复杂拓扑结构以及颅骨和相应面部之间的模糊关系，这是相当具有挑战性的。在本文中，我们提出了一种新的方法，利用条件生成对抗网络（CGAN）的基础上颅面深度图的三维颅面重建。更具体地说，我们把颅面重建作为一个映射问题，从头骨到脸。我们用深度图表示3D颅面形状，深度图包括用于识别目的的大多数颅面特征，并且易于生成和应用于神经网络。我们设计了一个基于CGAN的端到端神经网络模型，然后用配对的颅面数据训练模型，以自动学习颅骨和面部之间复杂的非线性关系。通过在CGAN中引入身体质量指数类（BMIC），可以实现基于颅骨的面部三维几何的客观重建，这是一个复杂的具有不同拓扑结构的三维形状生成任务。通过对比实验，我们的方法显示出准确性和逼真的颅面重建结果。
Craniofacial reconstruction is fundamental in resolving forensic cases. It is rather challenging due to the complex topology of the craniofacial model and the ambiguous relationship between a skull and the corresponding face. In this paper, we propose a novel approach for 3D craniofacial reconstruction by utilizing Conditional Generative Adversarial Networks (CGAN) based on craniofacial depth map. More specifically, we treat craniofacial reconstruction as a mapping problem from skull to face. We represent 3D cran- iofacial shapes with depth maps, which include most craniofacial features for identification purposes and are easy to generate and apply to neural networks. We designed an end-to-end neural networks model based on CGAN then trained the model with paired craniofacial data to automatically learn the complex nonlinear relationship between skull and face. By introducing body mass index classes(BMIC) into CGAN, we can realize objective reconstruction of 3D facial geometry according to its skull, which is a complicated 3D shape generation task with different topologies. Through comparative experiments, our method shows accuracy and verisimilitude in craniofacial reconstruction results.