README.md

---
license: apache-2.0
pipeline_tag: image-to-3d
---

<div align="center">
  <img src="assets/teaser.png" width="100%">

<h1>LingBot-Map: Geometric Context Transformer for Streaming 3D Reconstruction</h1>

Robbyant Team

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[![Paper](https://img.shields.io/static/v1?label=Paper&message=arXiv&color=red&logo=arxiv)](https://huggingface.co/papers/2604.14141)
[![PDF](https://img.shields.io/static/v1?label=Paper&message=PDF&color=red&logo=adobeacrobatreader)](lingbot-map_paper.pdf)
[![Project](https://img.shields.io/badge/Project-Website-blue)](https://technology.robbyant.com/lingbot-map)
[![HuggingFace](https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Model&message=HuggingFace&color=orange)](https://huggingface.co/robbyant/lingbot-map)
[![ModelScope](https://img.shields.io/static/v1?label=%F0%9F%A4%96%20Model&message=ModelScope&color=purple)](https://www.modelscope.cn/models/Robbyant/lingbot-map)
[![License](https://img.shields.io/badge/License-Apache--2.0-green)](LICENSE.txt)

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https://github.com/user-attachments/assets/fe39e095-af2c-4ec9-b68d-a8ba97e505ab

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### 🗺️ Meet LingBot-Map! We've built a feed-forward 3D foundation model for streaming 3D reconstruction! 🏗️🌍

LingBot-Map is a feed-forward 3D foundation model for reconstructing scenes from streaming data, built upon a geometric context transformer (GCT) architecture.

Key features include:
- **Geometric Context Transformer**: Architecturally unifies coordinate grounding, dense geometric cues, and long-range drift correction within a single streaming framework through anchor context, pose-reference window, and trajectory memory.
- **High-Efficiency Streaming Inference**: A feed-forward architecture with paged KV cache attention, enabling stable inference at ~20 FPS on 518×378 resolution over long sequences exceeding 10,000 frames.
- **State-of-the-Art Reconstruction**: Superior performance on diverse benchmarks compared to both existing streaming and iterative optimization-based approaches.

---

# ⚙️ Quick Start

## Installation

**1. Create conda environment**

```bash
conda create -n lingbot-map python=3.10 -y
conda activate lingbot-map
```

**2. Install PyTorch (CUDA 12.8)**

```bash
pip install torch==2.9.1 torchvision==0.24.1 --index-url https://download.pytorch.org/whl/cu128
```

**3. Install lingbot-map**

```bash
pip install -e .
```

**4. Install FlashInfer (recommended)**

FlashInfer provides paged KV cache attention for efficient streaming inference:

```bash
# CUDA 12.8 + PyTorch 2.9
pip install flashinfer-python -i https://flashinfer.ai/whl/cu128/torch2.9/
```

# 🎬 Demo

### Streaming Inference from Images

```bash
python demo.py --model_path /path/to/checkpoint.pt \
    --image_folder /path/to/images/
```

### Streaming Inference from Video

```bash
python demo.py --model_path /path/to/checkpoint.pt \
    --video_path video.mp4 --fps 10
```

### Streaming with Keyframe Interval

Use `--keyframe_interval` to reduce KV cache memory by only keeping every N-th frame as a keyframe.

```bash
python demo.py --model_path /path/to/checkpoint.pt \
    --image_folder /path/to/images/ --keyframe_interval 6
```

### Sky Masking

Sky masking filters out sky points from the reconstructed point cloud.

**Setup:**

```bash
pip install onnxruntime
```

**Usage:**

```bash
python demo.py --model_path /path/to/checkpoint.pt \
    --image_folder /path/to/images/ --mask_sky
```

# 📜 License

This project is released under the Apache License 2.0. See [LICENSE](LICENSE.txt) file for details.

# 📖 Citation

```bibtex
@article{chen2026geometric,
  title={Geometric Context Transformer for Streaming 3D Reconstruction},
  author={Chen, Lin-Zhuo and Gao, Jian and Chen, Yihang and Cheng, Ka Leong and Sun, Yipengjing and Hu, Liangxiao and Xue, Nan and Zhu, Xing and Shen, Yujun and Yao, Yao and Xu, Yinghao},
  journal={arXiv preprint arXiv:2604.14141},
  year={2026}
}
```

# ✨ Acknowledgments

This work builds upon several open-source projects:
- [VGGT](https://github.com/facebookresearch/vggt)
- [DINOv2](https://github.com/facebookresearch/dinov2)
- [Flashinfer](https://github.com/flashinfer-ai/flashinfer)