3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints. Existing methods often decompose 3D shapes into a sequence of localized components, treating each element in isolation without considering spatial consistency. As a result, these approaches exhibit limited versatility in 3D data representation and shape generation, hindering their ability to generate highly diverse 3D shapes that comply with the specified constraints. In this paper, we introduce a novel spatial-aware 3D shape generation framework that leverages 2D plane representations for enhanced 3D shape modeling. To ensure spatial coherence and reduce memory usage, we incorporate a hybrid shape representation technique that directly learns a continuous signed distance field representation of the 3D shape using orthogonal 2D planes. Additionally, we meticulously enforce spatial correspondences across distinct planes using a transformer-based autoencoder structure, promoting the preservation of spatial relationships in the generated 3D shapes. This yields an algorithm that consistently outperforms state-of-the-art 3D shape generation methods on various tasks, including unconditional shape generation, multi-modal shape completion, single-view reconstruction, and text-to-shape synthesis.

介绍了一种新型的空间感知的3D形状生成框架，利用2D平面表示增强3D形状建模，通过使用正交2D平面直接学习三维形状的连续有符号距离场表示，以确保空间一致性和减少内存使用，并通过基于Transformer的自编码结构精确实施不同平面之间的空间对应关系，促进生成的3D形状中空间关系的保留，从而在无条件形状生成、多模态形状补全、单视图重建和文本到形状合成等各种任务上始终优于最先进的3D形状生成方法。

NeuSDFusion：一种面向空间感知的生成模型用于3D形状补全、重构和生成