Update README.md

README.md

@@ -47,12 +47,6 @@ allowing the system to skip redundant frames early and significantly reduce comp
 - 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">
@@ -61,8 +55,105 @@ allowing the system to skip redundant frames early and significantly reduce comp
 
 ## Quick Start
 
-```python
-import torch
+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:
+
+```bash
+python3 scripts/download_checkpoints.py --output-root checkpoints --repo-id ShawnX98/LeanGate
+```
+
+This will place the checkpoint at:
+
+```text
+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`](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:
+
+```text
+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`
 
-ckpt = torch.load("model.pt", map_location="cpu")
-print(type(ckpt))