# SurGe：改进点映射中的表面几何

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-29 08:00
- AIHOT 分数：53
- AIHOT 链接：https://aihot.virxact.com/items/cmpvqe8rk00qjslukkdlcovrr
- 原文链接：https://arxiv.org/abs/2605.31577

## AI 摘要

SurGe 模型解决了 feedforward 3D 重建方法在点映射中局部表面几何不准确的问题。它引入了点映射法线指标来评估局部表面方向，并提出点梯度匹配损失和 Neighborhood Attention Decoder (NAD) 两个组件。在八个零样本单目几何基准测试中，SurGe 在全局点映射 AbsRel 上获得最佳平均排名，并一致改善局部点映射和法线评估。

## 正文

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.