https://github.com/pablo1n7/sketchzoomsdeep

Code for paper 'SketchZooms: Deep multi-view descriptors for matching line drawings'. January 2021
https://github.com/pablo1n7/sketchzoomsdeep

computer-graphics computer-science deep-learning paper-implementations pytorch

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Code for paper 'SketchZooms: Deep multi-view descriptors for matching line drawings'. January 2021

• Host: GitHub
• URL: https://github.com/pablo1n7/sketchzoomsdeep
• Owner: pablo1n7
• Created: 2021-02-22T17:09:16.000Z (over 5 years ago)
• Default Branch: main
• Last Pushed: 2021-04-30T13:58:45.000Z (about 5 years ago)
• Last Synced: 2025-01-21T07:11:23.189Z (over 1 year ago)
• Topics: computer-graphics, computer-science, deep-learning, paper-implementations, pytorch
• Language: Jupyter Notebook
• Homepage: https://onlinelibrary.wiley.com/doi/10.1111/cgf.14197
• Size: 1.7 MB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# SketchZooms: Deep multi-view descriptors for matching line drawings



  



## Abstract

Finding point-wise correspondences between images is a long-standing problem in computer vision. Corresponding sketch images is particularly challenging due to the varying nature of human style, projection distortions and viewport changes. In this paper we present a feature descriptor targeting line drawings learned from a 3D shape data set. Our descriptors are designed to locally match image pairs where the object of interest belongs to the same semantic category, yet still differ drastically in shape and projection angle. We build our descriptors by means of a Convolutional Neural Network (CNN) trained in a triplet fashion. The goal is to embed semantically similar anchor points close to one another, and to pull the embeddings of different points far apart. To learn the descriptors space, the network is fed with a succession of zoomed views from the input sketches. We have specifically crafted a data set of synthetic sketches using a non-photorealistic rendering algorithm over a large collection of part-based registered 3D models. Once trained, our network can generate descriptors for every pixel in an input image. Furthermore, our network is able to generalize well to unseen sketches hand-drawn by humans, outperforming state-of-the-art descriptors on the evaluated matching tasks. Our descriptors can be used to obtain sparse and dense correspondences between image pairs. We evaluate our method against a baseline of correspondences data collected from expert designers, in addition to comparisons with descriptors that have been proven effective in sketches. Finally, we demonstrate applications showing the usefulness of our multi-view descriptors.

[![arXiv](https://img.shields.io/badge/arXiv-1912.05019-f9f107.svg)](https://arxiv.org/abs/1912.05019)

[![DOI](https://img.shields.io/badge/DOI-10.1111/cgf.14197-f9f107.svg)](https://doi.org/10.1111/cgf.14197)

## Pipeline and Architecture Overview



  



> Left: Given a precompiled data set of 3D shapes augmented with correspondences data, we automatically generate line drawings at different scales and positions using a state-of-the-art non-photorealistic render engine. Right: We then take these line drawings as inputs for our convolutional neural network in order to learn local multi-view descriptors. Our triplet loss training scheme embeds semantically similar sketch points close together in descriptor space. Notice how matching points are mapped together independently of projection angle. Our multi-view architecture jointly with fully connected layers reduce the descriptor size while max pooling layers aggregate important information across input view

## Dataset

[Download the dataset](https://drive.google.com/drive/folders/1xguyQZqeHY_54NivqZ3jWLyG4fGOJ9au?usp=sharing) and put in `SketchZoomsDeep/data`.

      

```

SketchZoomsDeep

└── data

    ├── csv

    ├── pts_Airplane

    ├── pts_Table

    ├── ..

    ├── pts_Car

```

## Weights of a SketchZoom

Download the weights [here](https://drive.google.com/file/d/1ctfZJ0OlVqH1HGgiRSyYAHeGUjauWz2N/view?usp=sharing)

            

## Examples 

Run with Google Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1BEEtAu2ttyRV_6AvJtw7o6zVwYtUcrDt?usp=sharing)

Or run it locally using python 3.8.5 and installing the dependencies:

```bash

pip install - r requirements.txt

```

[How to create a network instance and how to use (Notebook)](https://github.com/pablo1n7/SketchZoomsDeep/blob/main/example_dataset.ipynb)

[Test matching (Notebook)](https://github.com/pablo1n7/SketchZoomsDeep/blob/main/test_match.ipynb)

## Train

In a terminal, launch visdom server to see the training progress

```bash

visdom

```

In another terminal, execute the training script:

```bash

python train_net.py --netname alexnet --nepoch 100 --lossname triplet --opt adm --lr 0.0001 --device cpu --envplotter main

```

*   **netname:** Type of network for training (alexnet, vgg, resnet).

*   **nepoch:** Number of epochs.

*   **lossname:** Type of loss (triplet, contrast).

*   **opt:** Type of optimizer (adm, sgd).

*   **lr:** Learning rate.

*   **device:** Type of device for train. (cpu, cuda).

*   **envplotter:** name of the environment inside of Visdom.

## Citation

```Latex

@article{https://doi.org/10.1111/cgf.14197,

author = {Navarro, Pablo and Orlando, J. Ignacio and Delrieux, Claudio and Iarussi, Emmanuel},

title = {SketchZooms: Deep Multi-view Descriptors for Matching Line Drawings},

journal = {Computer Graphics Forum},

volume = {n/a},

number = {n/a},

pages = {},

keywords = {image and video processing, 2D morphing, image and video processing, image databases, image and video processing},

doi = {https://doi.org/10.1111/cgf.14197},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14197},

eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.14197},

}

```