Surf-D: High-Quality Surface Generation for Arbitrary Topologies using Diffusion Models
CoRR(2023)
摘要
In this paper, we present Surf-D, a novel method for generating high-quality
3D shapes as Surfaces with arbitrary topologies using Diffusion models.
Specifically, we adopt Unsigned Distance Field (UDF) as the surface
representation, as it excels in handling arbitrary topologies, enabling the
generation of complex shapes. While the prior methods explored shape generation
with different representations, they suffer from limited topologies and
geometry details. Moreover, it's non-trivial to directly extend prior diffusion
models to UDF because they lack spatial continuity due to the discrete volume
structure. However, UDF requires accurate gradients for mesh extraction and
learning. To tackle the issues, we first leverage a point-based auto-encoder to
learn a compact latent space, which supports gradient querying for any input
point through differentiation to effectively capture intricate geometry at a
high resolution. Since the learning difficulty for various shapes can differ, a
curriculum learning strategy is employed to efficiently embed various surfaces,
enhancing the whole embedding process. With pretrained shape latent space, we
employ a latent diffusion model to acquire the distribution of various shapes.
Our approach demonstrates superior performance in shape generation across
multiple modalities and conducts extensive experiments in unconditional
generation, category conditional generation, 3D reconstruction from images, and
text-to-shape tasks.
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