---
language:
- en
tags:
- arxiv:2609.15795
- pytorch
- computer-vision
- 3d-reconstruction
- uncertainty-estimation
- streaming
datasets:
- tartanair
base_model: robbyant/lingbot-map
---

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

**Mingkai Liu, Hao Zhao, Xingxing Zuo**

[Paper](https://arxiv.org/abs/2609.15795) · [Code](https://github.com/RCL-Robotics/SURE-map) · [Project page](https://mingkai-liu.github.io/projects/sure-map/) · [Video](https://www.youtube.com/watch?v=vIKJFzLCtMc)

SURE-Map equips streaming geometric foundation models with cross-view geometric uncertainty and multi-timescale self-correction. Cross-view uncertainty assesses the geometric consistency of jointly predicted pose and depth, supporting dense-point filtering and local translation optimization. Sparse keyframe-window inference provides longer-range geometric evidence for scale recalibration.

## Checkpoint

`uncertainty.pt` is the trained **cross-view geometric uncertainty head** checkpoint used in the reported experiments. It is used together with the SURE-Map implementation and a separately downloaded LingBot-Map backbone; it is not a standalone reconstruction model.

The checkpoint is copied without modification from the official [SURE-Map repository](https://github.com/RCL-Robotics/SURE-map/tree/main/checkpoints).

- File size: 199,159,826 bytes.
- SHA-256: `a1b8cdd76ae33768e738554cece65b0113770e48824064c7a92b59752b06e1a7`.
- Training data: TartanAir v1; the backbone is frozen during uncertainty-head training.

## Download and use

Clone the code and follow its [environment setup instructions](https://github.com/RCL-Robotics/SURE-map#environment-setup):

```bash
git clone https://github.com/RCL-Robotics/SURE-map.git SURE-Map
cd SURE-Map
mkdir -p checkpoints
```

Download the uncertainty checkpoint into `checkpoints/uncertainty.pt`:

```bash
curl -L --fail \
  https://huggingface.co/milchstrasse/SURE-Map/resolve/main/uncertainty.pt \
  -o checkpoints/uncertainty.pt
```

Download the backbone separately:

```bash
curl -L --fail \
  https://huggingface.co/robbyant/lingbot-map/resolve/main/lingbot-map.pt \
  -o checkpoints/lingbot.pt
```

The SURE-Map inference code constructs `FlowSigmaHead` and loads the checkpoint's `sigma_head` state dictionary. Use the repository's inference and evaluation entry points rather than a Transformers `from_pretrained` call.

For example, after preparing KITTI and setting `dataset.root` in `online/configs/kitti.yaml`:

```bash
python online/run_kitti.py --config online/configs/kitti.yaml
```

See the [code repository](https://github.com/RCL-Robotics/SURE-map) for Oxford Spires, VBR, Neural RGB-D, 7-Scenes, and DTU preparation and evaluation instructions. This checkpoint is provided for the documented LingBot-Map-based configuration; compatibility with other backbones is not established.

## Citation

```bibtex
@misc{liu2026suremapselfcorrectingstreaminggeometric,
  title         = {SURE-Map: Self-Correcting Streaming Geometric Foundation Models},
  author        = {Mingkai Liu and Hao Zhao and Xingxing Zuo},
  year          = {2026},
  eprint        = {2609.15795},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2609.15795}
}
```

## Licensing

The upstream repository licenses its original source code under Apache-2.0 and preserves separate licenses for third-party components. It does not explicitly state a separate license for this checkpoint. See the upstream [license information](https://github.com/RCL-Robotics/SURE-map#license).