  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. Our project page is available at
https://weizheliu.github.io/NeuSDFusion/ .
