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/ .

翻译：三维形状生成旨在产生符合特定条件与约束的创新性三维内容。现有方法通常将三维形状分解为一系列局部化组件，孤立地处理每个元素而未考虑空间一致性。因此，这些方法在三维数据表示和形状生成方面表现出有限的多功能性，阻碍了其生成高度多样化且符合指定约束的三维形状的能力。本文提出了一种新颖的空间感知三维形状生成框架，该框架利用二维平面表示以增强三维形状建模。为确保空间连贯性并降低内存使用，我们引入了一种混合形状表示技术，该技术直接利用正交二维平面学习三维形状的连续符号距离场表示。此外，我们通过基于Transformer的自编码器结构精心强化不同平面间的空间对应关系，从而促进生成的三维形状中空间关系的保持。由此产生的算法在多项任务中持续优于最先进的三维形状生成方法，包括无条件形状生成、多模态形状补全、单视图重建以及文本到形状合成。我们的项目页面位于 https://weizheliu.github.io/NeuSDFusion/ 。