3DN：三维变形网络
3DN: 3D Deformation Network
摘要：
虚拟和增强现实中的应用产生了对快速创建和轻松访问大型3D模型集的需求。解决这一需求的有效方法是基于参考来编辑或变形现有的3D模型，例如，一个2D图像，这是很容易获得。给定这样的源3D模型和目标（可以是2D图像、3D模型或作为深度扫描获取的点云），我们引入3DN，这是一种端到端网络，它使源模型变形以类似于目标。我们的方法推断每顶点偏移位移，同时保持源模型的网格连接固定。我们提出了一种训练策略，它使用一种新的可微操作，网格采样算子，以推广我们的方法在源和目标模型具有不同的网格密度。网格采样算子可以无缝集成到网络中，以处理具有不同拓扑结构的网格。定性和定量的结果表明，我们的方法产生更高的质量的结果相比，最先进的基于学习的方法为3D形状生成。代码可在github.com/laughtervv/3DN上获得。
英文摘要：
Applications in virtual and augmented reality create a demand for rapid creation and easy access to large sets of 3D models. An effective way to address this demand is to edit or deform existing 3D models based on a reference, e.g., a 2D image which is very easy to acquire. Given such a source 3D model and a target which can be a 2D image, 3D model, or a point cloud acquired as a depth scan, we introduce 3DN, an end-to-end network that deforms the source model to resemble the target. Our method infers per-vertex offset displacements while keeping the mesh connectivity of the source model fixed. We present a training strategy which uses a novel differentiable operation, mesh sampling operator, to generalize our method across source and target models with varying mesh densities. Mesh sampling operator can be seamlessly integrated into the network to handle meshes with different topologies. Qualitative and quantitative results show that our method generates higher quality results compared to the state-of-the art learning-based methods for 3D shape generation. Code is available at github.com/laughtervv/3DN.
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