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ShawnX98/
LeanGate

slam

monocular-slam

visual-localization

geometric-foundation-models

pytorch

mit

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LeanGate

Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring

Project Page | Code | Paper Coming Soon

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

Method

Quick Start

This release lets you download the public LeanGate checkpoint, run LeanGate on prepared TUM, 7SCENES, or EUROC scenes, export sparse RGB manifests, and optionally launch MASt3R-SLAM on the filtered sequence.

1. Install

Use python3 and install a PyTorch version matching your CUDA runtime first.

bash

pip install -e .
pip install -e third_party/MASt3R-SLAM/thirdparty/mast3r
pip install -e third_party/MASt3R-SLAM/thirdparty/in3d
pip install --no-build-isolation -e third_party/MASt3R-SLAM

2. Download the released checkpoint

The public LeanGate checkpoint is hosted at:

Repo: ShawnX98/LeanGate
URL: https://huggingface.co/ShawnX98/LeanGate
File: leangate.pt

Download it with:

python3 scripts/download_checkpoints.py --output-root checkpoints --repo-id ShawnX98/LeanGate

This will place the checkpoint at:

checkpoints/leangate.pt

3. Run LeanGate on a prepared benchmark dataset

Example for TUM:

bash

python3 scripts/generate_rgb_lists.py \
  --dataset-type TUM \
  --dataset-root /data/tum \
  --output-root outputs/predictions \
  --device cuda:0

Supported benchmark inputs:

TUM
7SCENES
EUROC

Expected dataset layouts are documented in docs/dataset_layouts.md.

4. Run the plain RGB folder demo

For a simple folder of RGB frames:

bash

./demo.sh \
  --folder /data/my_rgb_frames \
  --output-root outputs/demo \
  --device cuda:0

This processes frames in sorted filename order and writes the filtered manifest to:

outputs/demo/leangate/<folder_name>.txt

5. Optional: launch MASt3R-SLAM on the sparse sequence

Single scene:

bash

python3 scripts/run_slam_scene.py \
  --dataset-type TUM \
  --dataset-root /data/tum \
  --scene-id rgbd_dataset_freiburg1_desk \
  --predictions-root outputs/predictions \
  --output-root outputs/slam

Full dataset:

bash

python3 scripts/run_slam_dataset.py \
  --dataset-type TUM \
  --dataset-root /data/tum \
  --predictions-root outputs/predictions \
  --output-root outputs/slam

Outputs

LeanGate inference produces:

outputs/predictions/<dataset_slug>/leangate/<scene>.txt
outputs/predictions/<dataset_slug>/leangate/scores/<scene>_scores.csv

Optional MASt3R-SLAM outputs include:

outputs/slam/<dataset_slug>/leangate/<scene>/trajectory_keyframes.tum
outputs/slam/<dataset_slug>/leangate/<scene>/reconstruction.ply
outputs/slam/<dataset_slug>/leangate/<scene>/run_metadata.json
outputs/slam/<dataset_slug>/leangate/summary.csv
outputs/slam/<dataset_slug>/leangate/summary.json