We introduce Mesh-RFT, a novel framework leveraging localized preference optimization with topology-aware scoring to achieve state-of-the-art high-fidelity 3D mesh generation.

OAT introduces the first step towards foundation models for topology optimization by combining a neural field auto-encoder and latent diffusion models with large scale training on a new general dataset of 2M optimized topologies called OpenTO.