Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshes
This addresses the problem of generating and reconstructing 3D meshes with controlled topology for applications in computer graphics and vision, representing a novel method for a known bottleneck.
The paper tackles 3D mesh reconstruction and generation by introducing DTNet, which learns topology-aware neural templates and decouples topology formulation from shape deformation. The method produces high-quality meshes with diverse topologies, outperforming state-of-the-art approaches.
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.