SurGe：點雲中改進的曲面幾何

摘要

近期前饋式三維重建方法在預測點雲與估計全域三維幾何方面表現出色。然而，其預測結果仍存在不準確的局部表面幾何，雖在視覺上明顯可辨，但在常用指標中僅微弱反映。為在評估中更明確凸顯此類誤差，我們引入一項點雲法向量指標，用以評估鄰近三維預測所誘導的局部表面朝向。為減少此類誤差，我們提出兩個互補組件：深度歸一化三維有限差分的點梯度匹配損失函數，以及逐步上採樣特徵並利用鄰域注意力進行局部特徵融合的鄰域注意力解碼器（NAD）。在八項零樣本單目幾何基準測試中，我們的模型 SurGe 在全域點雲相對絕對誤差（AbsRel）上取得最佳平均排名，並在局部點雲與點雲法向量評估中持續獲得改善。

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.