In industrial design, N-sided hole filling is typically formulated as the construction of a single trimmed B-spline surface by minimizing a fairness energy subject to geometric boundary constraints. This formulation requires an accurate parameter-space representation of the trimming curve on the filling surface. Most existing methods project the hole boundary onto a nearby plane or polygon to establish correspondence; however, they often neglect boundary heterogeneity, which can yield biased mappings, degrade fairness, and even cause filling failures. We propose UVTran, a transformer-based framework that predicts an auxiliary projection surface better to capture the geometric characteristics of the hole boundary. Exploiting B-spline locality, we design a cross-attention mechanism that biases each surface control point toward the nearby hole boundary, preserving local geometric detail. We voxelize control-point coordinates and formulate the fitting problem as a classification task, which reduces the model's sensitivity to small numerical perturbations and noise. We adopt a progressive-resolution training strategy that injects controlled discretization errors at coarse resolutions to mimic distribution shifts, thereby mitigating overfitting and improving generalization at high resolution. On our benchmark, UVTran outperforms both industrial and academic baselines: the tolerance-satisfaction rate improves by $12\%$, and it consistently produces fair filled surfaces even under complex hole boundary conditions. These results suggest that UVTran yields more faithful correspondences and fairer trimmed surfaces across a wide range of N-sided holes.

翻译：在工业设计中，N边孔洞填充通常被表述为通过最小化满足几何边界约束的光顺能量来构造单个裁剪B样条曲面。该表述要求填充曲面上裁剪曲线的精确参数空间表示。现有方法大多将孔洞边界投影到邻近平面或多边形上建立对应关系，但往往忽略边界异质性，这会导致映射偏差、降低光顺性，甚至引发填充失败。我们提出UVTran——一种基于Transformer的框架，通过预测辅助投影曲面以更好地捕捉孔洞边界的几何特征。利用B样条的局部性，我们设计了交叉注意力机制，使每个曲面控制点偏向于邻近孔洞边界，从而保留局部几何细节。我们将控制点坐标体素化，并将拟合问题重构为分类任务，从而降低模型对数值微小扰动和噪声的敏感性。我们采用渐进分辨率训练策略，在粗分辨率阶段注入受控的离散化误差以模拟分布偏移，从而缓解过拟合并提升高分辨率下的泛化能力。在我们的基准测试中，UVTran优于工业与学术基线方法：公差满足率提升12%，且即使在复杂孔洞边界条件下也能持续生成光顺的填充曲面。这些结果表明，UVTran能在广泛的N边孔洞场景中建立更可靠的对应关系并生成更光顺的裁剪曲面。