论文标题
神经模板：拓扑感知的重建和分离的3D网眼
Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshes
论文作者
论文摘要
本文介绍了一个名为DTNET的新型框架，用于通过DISENTANGLED拓扑结构进行3D网格重建和生成。除了以前的工作之外，我们还学习了每个输入特定的拓扑感知神经模板，然后将模板变形以重建详细的网格，同时保留学习的拓扑。一个关键的见解是将复杂的网格重建分解为两个子任务：拓扑配方和形状变形。多亏了脱钩，DT-NET隐含地学习了潜在空间中拓扑和形状的分离表示。因此，它可以启用新型的脱离控件来支持各种形状生成应用，例如，将3D对象的拓扑混合到以前的重建工作无法实现的3D对象的拓扑。广泛的实验结果表明，与最先进的方法相比，我们的方法能够产生高质量的网格，尤其是不同的拓扑。
This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology. Beyond previous works, we learn a topology-aware neural template specific to each input then deform the template to reconstruct a detailed mesh while preserving the learned topology. One key insight is to decouple the complex mesh reconstruction into two sub-tasks: topology formulation and shape deformation. Thanks to the decoupling, DT-Net implicitly learns a disentangled representation for the topology and shape in the latent space. Hence, it can enable novel disentangled controls for supporting various shape generation applications, e.g., remix the topologies of 3D objects, that are not achievable by previous reconstruction works. Extensive experimental results demonstrate that our method is able to produce high-quality meshes, particularly with diverse topologies, as compared with the state-of-the-art methods.
