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。