eadx / lingbot-map

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

---

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

LingBot-Map has focused on:

• 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

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

2. Install PyTorch (CUDA 12.8)

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

For other CUDA versions, see PyTorch Get Started .

3. Install lingbot-map

pip install -e .

4. Install FlashInfer (recommended)

FlashInfer provides paged KV cache attention for efficient streaming inference:

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

For other CUDA/PyTorch combinations, see FlashInfer installation . If FlashInfer is not installed, the model falls back to SDPA (PyTorch native attention) via --use_sdpa .

5. Visualization dependencies (optional)

pip install -e ".[vis]"

📦 Model Download

🎬 Demo

Streaming Inference from Images

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

Streaming Inference from Video

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. Non-keyframe frames still produce predictions but are not stored in the cache. This is useful for long sequences which excesses 320 frames.

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

Windowed Inference (for long sequences, >3000 frames)

python demo.py --model_path /path/to/checkpoint.pt \
    --video_path video.mp4 --fps 10 \
    --mode windowed --window_size 64

Sky Masking

Sky masking uses an ONNX sky segmentation model to filter out sky points from the reconstructed point cloud, which improves visualization quality for outdoor scenes.

Setup:

# Install onnxruntime (required)
pip install onnxruntime        # CPU
# or
pip install onnxruntime-gpu    # GPU (faster for large image sets)

The sky segmentation model ( skyseg.onnx ) will be automatically downloaded from HuggingFace on first use.

Usage:

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

Sky masks are cached in <image_folder>_sky_masks/ so subsequent runs skip regeneration.

Without FlashInfer (SDPA fallback)

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

📜 License

This project is released under the Apache License 2.0. See LICENSE file for details.

📖 Citation

@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

We thank Shangzhan Zhang, Jianyuan Wang, Yudong Jin, Christian Rupprecht, and Xun Cao for their helpful discussions and support.

This work builds upon several excellent open-source projects:

• VGGT
• DINOv2
• Flashinfer

---