Update README.md

README.md

@@ -1,7 +1,68 @@
 ---
-license: apache-2.0
+license: mit
+tags:
+- slam
+- monocular-slam
+- visual-localization
+- geometric-foundation-models
+- pytorch
+thumbnail: ./teaser.png
 ---
 
-Public LeanGate checkpoint.
+<p align="center">
+  <h1 align="center">LeanGate</h1>
+  <p align="center">
+    Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring
+  </p>
+</p>
 
-Filename: `leangate.pt`
+<p align="center">
+  <a href="https://lean-gate.github.io/">Project Page</a> |
+  <a href="https://github.com/XinmiaoShawn/LeanGate-code">Code</a> |
+  <a href="#">Paper Coming Soon</a>
+</p>
+
+<p align="center">
+  <img src="./teaser.png" width="95%">
+</p>
+
+## Overview
+
+LeanGate is a lightweight frame-gating model for transformer-based monocular SLAM.
+It predicts the geometric utility of an incoming frame before expensive dense reconstruction,
+allowing the system to skip redundant frames early and significantly reduce computation.
+
+## Highlights
+
+- Filters out more than 90% of redundant frames before heavy SLAM processing
+- Reduces tracking FLOPs by more than 85%
+- Achieves around 5x end-to-end throughput speedup
+- Maintains competitive tracking and mapping accuracy
+
+## Model
+
+- Checkpoint: `model.pt`
+- Framework: PyTorch
+- Task: frame utility scoring for monocular SLAM
+- Input: current frame and reference/keyframe features or paired image representation
+- Output: geometric utility score used for frame gating
+
+## Qualitative Results
+
+<p align="center">
+  <img src="./qualitative_2x3_ieee.png" width="95%">
+</p>
+
+## Method
+
+<p align="center">
+  <img src="./system.png" width="95%">
+</p>
+
+## Quick Start
+
+```python
+import torch
+
+ckpt = torch.load("model.pt", map_location="cpu")
+print(type(ckpt))