We present Surf-D, a novel method for generating high-quality 3D shapes as Surfaces with arbitrary topologies using Diffusion models. Previous methods explored shape generation with different representations and they suffer from limited topologies and poor geometry details. To generate high-quality surfaces of arbitrary topologies, we use the Unsigned Distance Field (UDF) as our surface representation to accommodate arbitrary topologies. Furthermore, we propose a new pipeline that employs a point-based AutoEncoder to learn a compact and continuous latent space for accurately encoding UDF and support high-resolution mesh extraction. We further show that our new pipeline significantly outperforms the prior approaches to learning the distance fields, such as the grid-based AutoEncoder, which is not scalable and incapable of learning accurate UDF. In addition, we adopt a curriculum learning strategy to efficiently embed various surfaces. With the pretrained shape latent space, we employ a latent diffusion model to acquire the distribution of various shapes. Extensive experiments are presented on using Surf-D for unconditional generation, category conditional generation, image conditional generation, and text-to-shape tasks. The experiments demonstrate the superior performance of Surf-D in shape generation across multiple modalities as conditions. Visit our project page at https://yzmblog.github.io/projects/SurfD/.

翻译：本文提出Surf-D，一种利用扩散模型生成任意拓扑结构高质量三维形状曲面的新方法。先前方法探索了不同表示形式的形状生成，但受限于拓扑结构的单一性和几何细节的不足。为生成任意拓扑结构的高质量曲面，我们采用无符号距离场作为曲面表示以适应任意拓扑。进一步提出一种新流程，利用基于点的自编码器学习紧凑连续的潜在空间，以精确编码UDF并支持高分辨率网格提取。我们证明该流程在距离场学习方面显著优于先前方法（如基于网格的自编码器），后者不具备可扩展性且无法准确学习UDF。此外，我们采用课程学习策略以高效嵌入各类曲面。借助预训练的形狀潜在空间，采用潜在扩散模型学习多种形状的分布。通过大量实验验证了Surf-D在无条件生成、类别条件生成、图像条件生成及文本到形状任务中的表现。实验表明Surf-D在多模态条件形状生成方面具有卓越性能。项目页面请访问：https://yzmblog.github.io/projects/SurfD/。