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Leveraging Pixel Correspondences for Sparse-to-Dense Feature-Metric Localization

This repo contains the code for our 3D Vision project.
Developed by Zimeng Jiang, Le Chen and Lixin Xue from Computer Vision and Geometry Group (CVG) at ETHz.

You can find our final report here.

1 Description:

Visual localization is a key component to many robotics systems. However, it is very challenging in changing conditions such as day-night or summer-winter. Based on Sparse-to-Dense Hypercolumn Matching, we improve the localization accuracy by (1) performing Feature-metric PnP given an initial estimation of the pose together with dense deep features of images and (2) training on the supervision of pixel correspondences using double margin contrastive loss and Gauss-Newton loss to generate better feature maps. Experimental results show that Feature-metric PnP refines pose estimation and we achieve our best accuracy when combine it with features trained on correspondences. This repository is training code of the whole visual localization pipeline. You can find the evaluation part here.

2 Data root

You can download the dataset here. You need to follow directory structure of the data as below.

${gn_net root}
├── data
├── ├── robotcar
|   `── ├── correspondence/
|       |   ├── *.mat
|       ├── images
│           ├── overcast-reference/
│           ├── overcast-summer/
│           ├── overcast-winter/
│           ├── sun/
│           ├── dawn/
│           ├── rain/
│           ├── snow/
│           ├── dusk/
│           ├── night/
│           └── night-rain/
├── ├── cmu
|   `── ├── correspondence/
|       |   ├── *.mat
|       ├── images
│           ├── slice6/
│           ├── slice7/
│           ├── slice8/
            ...

3 Getting Started:

Run the following commands to install this repository and the required dependencies:

git clone https://github.com/zimengjiang/gn_net.git
cd gn_net/
pip3 install -r requirements.txt

This code was run and tested on Python 3.7.3, using Pytorch 1.5.1 although it should be compatible with some previous versions. You can follow instructions to install Pytorch here. Please configure the data root and save root before training! You can change the parameters in run.py. The vgg_checkpoint is the original weight provided by S2DHM. You can download the checkpoint files from here. And please remember to set up the path to vgg_checkpoint in run.py.

python run.py

4 Pipeline:

The whole pipeline consists of this repository and the S2DHM with Feature-PnP.

5 Evaluation:

You can evaluate the checkpoint files using the S2DHM with Feature-PnP.

6 Code references:

Part of this repository is based on the official S2DHM and UNET repositories.