README

LingBot-Map: Geometric Context Transformer for Streaming 3D Reconstruction

Robbyant Team















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 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.

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 📑 Table of Contents


Click to expand

- 📰 News
- 📋 TODO
- ⚙️ Installation
- 📦 Model Download
- 🚀 Quick Start
- 🎬 Interactive Demo (demo.py)
  - Try the Example Scenes
  - Streaming with Keyframe Interval
  - Windowed Inference (for long sequences, 3000 frames)
  - Sky Masking
  - Visualization Options
  - Performance & Memory
- 🎥 Offline Rendering Pipeline (demo_render/batch_demo.py)
- 📜 License
- 📖 Citation
- ✨ Acknowledgments



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 📰 News

- 2026-06-28 — Fixed an SDPA KV cache bug. The SDPA backend now performs better on long sequences. We still recommend the FlashInfer backend for the best performance.
- 2026-05-25 — 📊 Evaluation benchmark released. We released the evaluation scripts for KITTI and Oxford Spires — see benchmark/ for the pipeline, and run preprocess/oxford.py to prepare Oxford Spires data before evaluation.
- 2026-04-29 — 📹 Long-video demo released. We released a very-long-video example (~25 000 frames, 13-minute indoor walkthrough) rendered with the offline pipeline — see Worked Example for the command, flag rationale, and rendered output.
- 2026-04-27 — 🚀 LingBot-Map accelerated. Pull the latest main and run python demo.py --compile ... or python gct_profile.py --backend flashinfer --dtype bf16 --compile to verify on your hardware.
- 2026-04-24 — Fixed a FlashInfer KV cache bug where --keyframe_interval  1 silently cached non-keyframes. You should now see better pose and reconstruction quality when running with more than 320 frames.

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 📋 TODO

- ✅ Release evaluation benchmark
  - ✅ Oxford Spires dataset
  - ✅ KITTI dataset
  - ✅ VBR dataset
  - ✅ Droid-W dataset
  - ✅ TUM-D dataset
  - ✅ 7-scenes dataset
  - ✅ ETH3D dataset
  - ✅ Tanks and Temples dataset
  - ✅ NRGBD dataset
- ✅ Release demo scripts
  - ✅ Indoor long-video demo (Featured indoor walkthrough)
  - ✅ Outdoor long-video demo
  - ✅ LingBot-World demo (Worked example)
  - ✅ Aerial long-video demo

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 ⚙️ Installation

1. Create conda environment

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


2. Install PyTorc