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：我们提出Surf-D，一种利用扩散模型生成具有任意拓扑的高质量三维形状（以曲面形式表示）的新方法。先前的方法探索了不同表示下的形状生成，但受限于有限的拓扑结构和较差的几何细节。为生成任意拓扑的高质量曲面，我们采用无符号距离场（UDF）作为曲面表示以兼容任意拓扑。此外，我们提出一种新流程，使用基于点的自编码器学习紧凑且连续的潜在空间，从而精确编码UDF并支持高分辨率网格提取。我们进一步证明，该新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），后者缺乏可扩展性且无法学习精确的UDF。同时，我们采用课程学习策略高效嵌入多种曲面。借助预训练的形状潜在空间，我们使用潜在扩散模型获取多样形状的分布。我们通过大量实验展示了Surf-D在无条件生成、类别条件生成、图像条件生成以及文本到形状任务中的表现。实验证明Surf-D在多种模态条件下的形状生成中具有优越性能。访问我们的项目页面：https://yzmblog.github.io/projects/SurfD/。