HierRelTriple：利用层次化关系三元组损失指导室内布局生成
HierRelTriple: Guiding Indoor Layout Generation with Hierarchical Relationship Triplet Losses
AI总结：
提出层次化关系三元组建模框架HierRelTriple，通过提取O2R、O2O和C2C三级空间关系并计算IoU损失集成至扩散去噪过程，显著提升了室内布局生成的物理真实性与空间关系指标。
摘要：
我们提出了一种基于三元组的层次化室内关系学习方法，命名为HierRelTriple，重点关注空间关系学习。现有方法通常依赖人工定义的空间规则或简化的成对表示，无法捕捉真实场景中复杂的、多物体间的关系，导致布局拥挤或物理上不合理。我们引入了HierRelTriple，一个层次化关系三元组建模框架，该框架首先划分功能区域，然后自动提取三个层次的空间关系：物体到区域（O2R）、物体到物体（O2O）和角点到角点（C2C）。通过将这些关系表示为几何三元组，并采用基于Delaunay Triangulation的方法建立空间先验，我们计算了去噪三元组与真实三元组之间的IoU损失，并将其无缝集成到扩散去噪过程中。物体间距离、角度方向和空间关系的联合公式的引入，增强了生成场景的物理真实性。在无条件布局合成、基于平面图的布局生成和场景重排的大量实验表明，与最先进方法相比，HierRelTriple将空间关系指标提升了15%以上，并大幅减少了碰撞和边界违规现象。
英文摘要：
We present a hierarchical triplet-based indoor relationship learning method, coined HierRelTriple, with a focus on spatial relationship learning. Existing approaches often depend on manually defined spatial rules or simplified pairwise representations, which fail to capture complex, multi-object relationships found in real scenarios and lead to overcrowded or physically implausible arrangements. We introduce HierRelTriple, a hierarchical relational triplets modeling framework that first partitions functional regions and then automatically extracts three levels of spatial relationships: object-to-region (O2R), object-to-object (O2O), and corner-to-corner (C2C). By representing these relationships as geometric triplets and employing approaches based on Delaunay Triangulation to establish spatial priors, we derive IoU loss between denoised and ground truth triplets and integrate them seamlessly into the diffusion denoising process. The introduction of the joint formulation of inter-object distances, angular orientations, and spatial relationships enhances the physical realism of the generated scenes. Extensive experiments on unconditional layout synthesis, floorplan-conditioned layout generation, and scene rearrangement demonstrate that HierRelTriple improves spatial-relation metrics by over 15% and substantially reduces collisions and boundary violations compared to state-of-the-art methods.
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