The availability of affordable and portable depth sensors has made scanning
objects and people simpler than ever. However, dealing with occlusions and
missing parts is still a significant challenge. The problem of reconstructing a
(possibly non-rigidly moving) 3D object from a single o
本篇论文提出一种数据驱动的方法,结合使用体积深度神经网络和 3D 形状合成来完成部分扫描的 3D 形状。该方法使用 3D 卷积层来对填充缺失数据进行预测,旨在在未知区域精确地预测全局结构,并使用来自形状数据库的 3D 几何图形进行中间结果的相关处理。最后,通过基于补丁的 3D 形状合成方法,根据 3D-EPN 获取的全局网格结构作为约束,生成高分辨率的输出,重建精细的细节。