用语义桥接和对称约束，让潦草草图生成更准确的3D形状。
sketch2symm: Symmetry-aware sketch-to-shape generation via semantic bridging
- 先通过草图转图像增强语义信息，再结合对称性生成结构一致的3D模型。
- 在主流数据集上，三项指标均优于现有方法，尤其对称结构更精准。
- 适合做草图到3D建模的开发者或研究者，尤其关注几何一致性场景。
基于草图的3D重建因草图输入抽象且稀疏，常缺乏足够的语义与几何信息而面临挑战。为此，我们提出Sketch2Symm，一种两阶段生成方法，从草图生成几何一致的3D形状。该方法通过草图到图像的语义桥接，丰富稀疏草图表示，并引入对称性约束作为几何先验，利用日常物体中常见的结构规律。在主流草图数据集上的实验表明，我们的方法在Chamfer Distance、Earth Mover's Distance和F-Score三项指标上均优于现有草图重建方法，验证了所提语义桥接与对称感知设计的有效性。
原文摘要 · Abstract (English)
Sketch-based 3D reconstruction remains a challenging task due to the abstract and sparse nature of sketch inputs, which often lack sufficient semantic and geometric information. To address this, we propose Sketch2Symm, a two-stage generation method that produces geometrically consistent 3D shapes from sketches. Our approach introduces semantic bridging via sketch-to-image translation to enrich sparse sketch representations, and incorporates symmetry constraints as geometric priors to leverage the structural regularity commonly found in everyday objects. Experiments on mainstream sketch datasets demonstrate that our method achieves superior performance compared to existing sketch-based reconstruction methods in terms of Chamfer Distance, Earth Mover's Distance, and F-Score, verifying the effectiveness of the proposed semantic bridging and symmetry-aware design.
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