[new] A colab demo
of ECO-TR is provided here.
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement, ECCV2022
Dongli Tan*, Jiang-Jiang Liu*, Xingyu Chen, Chao Chen, Ruixin Zhang, Yunhang Shen, Shouhong Ding, Rongrong Ji
We propose an efficient structure named Efficient Correspondence Transformer ECO-TR by finding correspondences in a coarse-to-fine manner, which significantly improves the efficiency of functional model methods. To achieve this, multiple transformer blocks are stage-wisely connected to gradually refine the predicted coordinates upon a shared multi-scale feature extraction network. All the correspondences are predicted within a single feed-forward pass, given a pair of images and for arbitrary query coordinates. We further propose an adaptive query-clustering strategy and an uncertainty-based outlier detection module to cooperate with the proposed framework for faster and better predictions. Experiments on various sparse and dense matching tasks demonstrate the superiority of our method in both efficiency and effectiveness against existing functional matching methods.
Clone the repo:
git clone https://github.com/dltan7/ECO-TR.git
download model weights from here
and prepare the environment below:
conda env create -f environment.yaml
conda activate eco-tr- Matching demo
An example is given in demos/match_pair.py.
- notebook
Two notebook examples are given in notebooks.
This repo is under refactoring. We will release other codes later.
Part of the model and evaluation codes are borrowed or ported from COTR, LoFTR, and DenseMatching. Please also cite these works if you find the corresponding code useful.