SurGe：点地图中改进的表面几何

摘要

近期前馈式三维重建方法在预测点图及估计全局三维几何结构方面表现相当出色。然而，其预测结果仍存在局部表面几何不准确的问题——这些问题在定性观察中显而易见，但在常规评价指标中却仅能微弱体现。为使这些误差在评估中更加明晰，我们提出了一种点图法向量度量指标，用于评估邻域三维预测所诱导的局部表面朝向。为减少此类误差，我们提出两个互补组件：一是点梯度匹配损失函数，用于监督深度归一化的三维有限差分；二是邻域注意力解码器（NAD），该模块通过渐进式上采样特征并运用邻域注意力实现局部特征融合。在八项零样本单目几何基准测试中，我们的模型SurGe在全局点图AbsRel指标上取得了最佳平均排名，同时在局部点图与点图法向量评估中均实现持续改进。

English

Recent feedforward 3D reconstruction methods predict point maps and estimate global 3D geometry remarkably well. However, their predictions still exhibit inaccurate local surface geometry, which is clearly visible qualitatively but only weakly reflected in common metrics. To make these errors more explicit in evaluation, we introduce a point map normal metric that evaluates the local surface orientation induced by neighboring 3D predictions. To reduce these errors, we propose two complementary components: a point gradient matching loss that supervises depth-normalized 3D finite differences, and a Neighborhood Attention Decoder (NAD) that progressively upsamples features and uses Neighborhood Attention for local feature mixing. Across eight zero-shot monocular geometry benchmarks, our model, SurGe, achieves the best average rank for global point map AbsRel and consistently improves local point map and point map normal evaluations.