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.
翻译:本文引入了一个名为 DTNet 的新框架, 用于 3D 网格重建, 并通过分散的地形学生成 。 除了先前的工程外, 我们学习了每个输入中特有的表层敏神经模板, 然后将模板变形, 以重建一个详细的网格, 同时保存学到的地形学。 一个关键的洞察力是将复杂的网格重建分解成两个子任务: 地形学配方和形状变形。 由于脱钩, DT- 网暗地学习了隐蔽的表层和形状在潜在空间中的分解代表。 因此, 它能够为各种形状生成应用提供新颖的分解控制, 比如, 重新混合 3D 对象的表层, 而以前的重建工程是无法实现的 。 广泛的实验结果表明, 我们的方法能够产生高品质的 meshes, 特别是与各种地形相比, 能够产生高品质的 meshes, 。