lingbot-map

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

Robbyant Team

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[![Conference Version Paper](https://img.shields.io/static/v1?label=Conference%20Version&message=Paper&color=blue)](https://linzhuo.xyz/gct.pdf)
[![Technical Report Version Paper](https://img.shields.io/static/v1?label=Technical%20Report%20Version&message=arXiv&color=red)](https://arxiv.org/abs/2604.14141)
[![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 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

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- [📰 News](#-news)
- [📋 TODO](#-todo)
- [⚙️ Installation](#️-installation)
- [📦 Model Download](#-model-download)
- [🚀 Quick Start](#-quick-start)
- [🎬 Interactive Demo (`demo.py`)](#-interactive-demo-demopy)
  - [Try the Example Scenes](#try-the-example-scenes)
  - [Streaming with Keyframe Interval](#streaming-with-keyframe-interval)
  - [Windowed Inference (for long sequences, >3000 frames)](#windowed-inference-for-long-sequences-3000-frames)
  - [Sky Masking](#sky-masking)
  - [Visualization Options](#visualization-options)
  - [Performance & Memory](#performance--memory)
- [🎥 Offline Rendering Pipeline (`demo_render/batch_demo.py`)](#-offline-rendering-pipeline-demo_renderbatch_demopy)
- [📜 License](#-license)
- [📖 Citation](#-citation)
- [✨ Acknowledgments](#-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 rel