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Fast Relative Pose Estimation using Relative Depth

Presented at 3DV 2024

Poster | Paper

This repository hosts the official implementation of the paper Fast Relative Pose Estimation using Relative Depth. It includes a 3-point minimal solver for relative pose implemented in PoseLib, a neural network (RelScaleNet) for estimating relative scales, and code to demonstrate their usage. We also include code for running evaluation on pre-computed SuperPoint+SuperGlue keypoints on ScanNet.

Jonathan Astermark · Yaqing Ding · Viktor Larsson · Anders Heyden

Setup

Create Conda environment

conda env create --file environment.yml
conda activate reldepth

Get pre-trained weights for RelScaleNet

wget -N -P weights https://vision.maths.lth.se/jastermark/relscalenet/weights/model_final.pth

Install solver

The 3-point solver is implemented in PoseLib. To install it, run

git submodule update --init --recursive
cd PoseLib
pip install .
cd ..

Demos

Demo on single image pair

A simple demo on a single image pair is implemented in demo.ipynb.

Evaluation on ScanNet-1500

We provide code to evaluate RelScaleNet and our 3-point solver on ScanNet-1500 by following the steps below. Note that the results will differ slightly from the paper, as a different RANSAC-implementation was used.

  1. Download the ScanNet-1500 images and pre-extracted SuperPoint+SuperGlue keypoints.
cd data
bash download_scannet.sh
cd ..

In the paper we used SuperPoint+SuperGlue keypoints extracted at 640x480 pixels, which led to matches with a lower inlier ratio. If you would like to use the same keypoints we used in the paper, they are available here:

wget -N http://vision.maths.lth.se/viktor/posebench/relative/scannet1500_spsg_old.h5
  1. (Optional) Pre-compute RelScaleNet estimations on the SP+SG keypoints. The results are stored in a *.h5-file.
python precompute_relscalenet.py

  1. Run the notebook evaluate_relscalenet.ipynb to evaluate RelScaleNet on SP+SG keypoints on ScanNet.

  2. Run the notebook evaluate_solver.ipynb to evaluate our 3-point solver on SP+SG keypoints on ScanNet.

BibTeX Citation

If you find our method useful, please consider citing our paper.

@inproceedings{astermark2024fast,
  author    = {Astermark, Jonathan and 
               Ding, Yaqing and
               Larsson, Viktor and
               Heyden, Anders},
  title     = {Fast Relative Pose Estimation using Relative Depth},
  booktitle = {3DV},
  year      = {2024}
}