Implicit generative models have been widely employed to model 3D data and have recently proven to be successful in encoding and generating high-quality 3D shapes. This work builds upon these models and alleviates current limitations by presenting the first implicit generative model that facilitates the generation of complex 3D shapes with rich internal geometric details. To achieve this, our model uses unsigned distance fields to represent nested 3D surfaces allowing learning from non-watertight mesh data. We propose a transformer-based autoregressive model for 3D shape generation that leverages context-rich tokens from vector quantized shape embeddings. The generated tokens are decoded into an unsigned distance field which is rendered into a novel 3D shape exhibiting a rich internal structure. We demonstrate that our model achieves state-of-the-art point cloud generation results on popular classes of 'Cars', 'Planes', and 'Chairs' of the ShapeNet dataset. Additionally, we curate a dataset that exclusively comprises shapes with realistic internal details from the `Cars' class of ShapeNet and demonstrate our method's efficacy in generating these shapes with internal geometry.

翻译：隐式生成模型已被广泛用于建模三维数据，并近期在编码和生成高质量三维形状方面展现出成功。本研究基于这些模型，通过提出首个能够生成具有丰富内部几何细节的复杂三维形状的隐式生成模型，缓解了当前方法的局限性。为实现这一目标，我们的模型采用无符号距离场来表示嵌套的三维曲面，从而能够从非水密网格数据中学习。我们提出了一种基于Transformer的自回归模型用于三维形状生成，该模型利用来自向量量化形状嵌入的上下文丰富令牌。生成的令牌被解码为无符号距离场，进而渲染为具有丰富内部结构的新型三维形状。实验表明，我们的模型在ShapeNet数据集中的“汽车”、“飞机”和“椅子”等主流类别上实现了最先进的点云生成结果。此外，我们专门整理了一个仅包含ShapeNet“汽车”类别中具有真实内部细节形状的数据集，并验证了该方法在生成带有内部几何结构的形状方面的有效性。