# Evaluation with LGT-Net  
This is instruction for evaluating our dataset with "[LGT-Net: Indoor Panoramic Room Layout Estimation with Geometry-Aware Transformer Network](https://arxiv.org/abs/2203.01824)".

# Downloading Pre-trained Weights
Pre-trained weights are provided by authors on individual datasets at [here](https://drive.google.com/drive/folders/1bOZyXeuNnwFEC9nw7EgJUwMiI685obdT?usp=sharing).  
These models are used in our dataset paper below.  
- [mp3d/best.pkl](https://drive.google.com/file/d/1o97oAmd-yEP5bQrM0eAWFPLq27FjUDbh/view?usp=sharing): Training on MatterportLayout dataset
- [pano/best.pkl](https://drive.google.com/file/d/1JoeqcPbm_XBPOi6O9GjjWi3_rtyPZS8m/view?usp=sharing): Training on PanoContext(train)+Stanford2D-3D(whole) dataset
- [s2d3d/best.pkl](https://drive.google.com/file/d/1PfJzcxzUsbwwMal7yTkBClIFgn8IdEzI/view?usp=sharing): Training on Stanford2D-3D(train)+PanoContext(whole) dataset

Make sure the pre-trained weight files are stored as follows:
```
checkpoints
|-- SWG_Transformer_LGT_Net
|   |-- mp3d
|   |   |-- best.pkl
|   |-- pano
|   |   |-- best.pkl
|   |-- s2d3d
|   |   |-- best.pkl
```

# Preparing Dataset  
You can use `assets/layout_eval/convert4LGTNet.py` to get proper data structure for evaluation.

### Evaluation with MatterportLayout  
Make sure the dataset files are stored as follows:
```
src/dataset/mp3d
|-- image
|   |-- 000_<scene>_equi_rgb.png
|-- label
|   |-- 000_<scene>_equi_layout.json
|-- split
    |-- test.txt # it needs to contain all of files.
```

### Evaluation with PanoContext 
Make sure the dataset files are stored as follows:
```
src/dataset/pano_s2d3d
|-- test
|   |-- img
|   |   |-- pano_000_<scene>_equi_rgb.png
|   |-- label_cor
|       |-- pano_000_<scene>_equi_layout.txt
```

### Evaluation with Stanford 2D-3D  
Make sure the dataset files are stored as follows:
```
src/dataset/pano_s2d3d
|-- test
|   |-- img
|   |   |-- camera_000_<scene>_equi_rgb.png
|   |-- label_cor
|       |-- camera_000_<scene>_equi_layout.txt
```