# SURE-Map：自校正流式几何基础模型

> 原文链接：https://robotics.hellobike.com/papers/2026-09-14-2609-15795
> 频道：papers | 更新日期：2026-09-14 | 标签：arxiv, cs.RO
> 核心摘要：SURE-Map通过跨视角几何不确定性与多时间尺度自校正，解决流式重建误差累积，并在长时程基准验证。

# SURE-Map：自校正流式几何基础模型

::: v-pre
> 原文链接：[SURE-Map: Self-Correcting Streaming Geometric Foundation Models](https://arxiv.org/abs/2609.15795)（arXiv 2609.15795 · 发布日期：2026-09-14）

**SURE-Map: Self-Correcting Streaming Geometric Foundation Models**

SURE-Map通过跨视角几何不确定性与多时间尺度自校正，解决流式重建误差累积，并在长时程基准验证。

## 核心要点

- 流式几何基础模型因上下文有限，易受动态物体和弱纹理影响，局部误差会累积为畸变与尺度漂移。
- 方法显式建模跨视角几何不确定性，衡量预测位姿和深度是否产生一致像素对应。
- 采用连续帧快速推理保持流式效率，并用稀疏关键帧窗口周期性重校近期轨迹尺度。
- 在KITTI、Oxford Spires和VBR上刷新在线前馈重建表现，闭环优化可进一步降低ATE-RMSE。

**验证范围与局限**：验证范围为KITTI、Oxford Spires和VBR长时程在线前馈重建基准。

## 英文摘要 (Abstract)

Streaming geometric foundation models are emerging as a compelling alternative to SLAM systems. Yet this streaming nature introduces a fundamental issue: each prediction is made from limited context, which is vulnerable to dynamic objects and weak textures. Small local errors accumulate into severe geometric distortion and long-horizon scale drift. We argue that reliable streaming reconstruction requires geometric foundation models to be not only predictive, but also self-correcting. We introduce SURE-Map, a self-correcting framework built upon two complementary principles. First, we explicitly model cross-view geometric uncertainty. Unlike conventional depth or point confidence, which primarily reflects the reliability of individual-view prediction, our uncertainty directly measures whether the jointly predicted pose and depth induce geometrically consistent cross-view pixel correspondences. Second, because local correction alone cannot eliminate slowly accumulating scale errors, we introduce multi-timescale self-correction: fast consecutive-frame inference preserves streaming efficiency, while sparse keyframe-window inference provides longer-range geometric evidence to periodically recalibrate the scale of recent trajectories. SURE-Map establishes new state-of-the-art performance for online feed-forward reconstruction across long-horizon benchmarks, reducing ATE-RMSE from 24.00 to 17.24 m on KITTI, 5.11 to 4.74 m on Oxford Spires, and 31.37 to 28.58 m on VBR, with further improvements to 15.17, 4.63, and 22.12 m when incorporating loop-closure refinement. Project page: https://mingkai-liu.github.io/projects/sure-map/.

作者：Mingkai Liu, Hao Zhao, Xingxing Zuo
:::

## 原始参考来源 (Sources)

- [arXiv 2609.15795：SURE-Map: Self-Correcting Streaming Geometric Foundation Models](https://arxiv.org/abs/2609.15795)