L2D2: Learnable Line Detector and Descriptor

This repository contains the pre-trained network implementation of the following paper:

Please cite this publication whenever you use the implementation:

@inproceedings{l2d2,
  author    = {Hichem Abdellali, Robert Frohlich, Viktor Vilagos, Zoltan Kato},
  title     = {{L2D2:} Learnable Line Detector and Descriptor},
  booktitle = {IEEE International Conference on 3D Vision (3DV)},
  address      = {{United kingdom, London} (online)},  
  year      = {2021},
}

The repository contains the full L2D2 pipeline which includes: the pre-trained detector/descriptor python code, the line extraction matlab code and the patch creation matlab code.

For the Matlab code, the Package contains all the necessary functions.

Getting started

You just need a conda environment with Python 3.7.9

conda env create -f l2d2.yml

The full L2D2 pipeline needs input images of 512x512 pixels (an example is provided inside IN_OUT_DATA/INPUT_IMAGES/)

To obtain the detected lines and the descriptors, run the 4 programs which compose the full L2D2 pipeline in the following orders

(l2d2) > python main_inference_detector.py
>> line_extraction_heatmap.m  (matlab code)
>> main_Patch_creation.m (matlab code)
(l2d2) > python main_Inference_descriptor.py

1. main_inference_detector.py located inside 1_Pre_trained_detector: loads the images from the folder INPUT_IMAGES and provides a heatmap of 512x512 for each input image in HEATMAPS_DIR
2. line_extraction_heatmap.m located inside 2_Line_extraction: loads the heatmaps from HEATMAPS_DIR and provides the detected lines in a .mat file in the EXTRACTED_LINES folder
3. main_Patch_creation.m located inside 3_Patch_extraction: reads the lines from the folder EXTRACTED_LINES of each image and provides a patch of 48x32 for each 2D line, the patches are stored in the EXTRACTED_PATCHES folder
4. main_Inference_descriptor.py located inside 4_Pre_trained_descriptor: loads the patches from the folder EXTRACTED_PATCHES and provides the descriptor in folder DESCRIPTORS`

All the input/output data is located inside the folder IN_OUT_DATA

Pretrained models

We provided the pre-trained models in the IN_OUT_DATA/INPUT_NETWEIGHT/ folder:

• checkpoint_line_detector.pth.tar: this is the pre-trained model for the line detector.
• checkpoint_line_descriptor.th: this is the pre-trained model for the line descriptor.

NOTE: the detector/descriptor training code and the training data generation code will be released later