# Learned Collaborative Stereo Refinement

## TL;DR

This paper introduces a learning-based variational network that refines and denoises disparity maps in stereo images by leveraging joint statistics of color, confidence, and disparity, with demonstrated effectiveness on benchmark datasets.

## Contribution

The work presents a novel variational network derived from unrolled proximal gradient iterations, enabling collaborative regularization and interpretability in disparity map refinement.

## Key findings

- Effective disparity refinement on Middlebury 2014 benchmark
- Improved results on Kitti 2015 stereo benchmark
- Method offers interpretability through its variational structure

## Abstract

In this work, we propose a learning-based method to denoise and refine disparity maps of a given stereo method. The proposed variational network arises naturally from unrolling the iterates of a proximal gradient method applied to a variational energy defined in a joint disparity, color, and confidence image space. Our method allows to learn a robust collaborative regularizer leveraging the joint statistics of the color image, the confidence map and the disparity map. Due to the variational structure of our method, the individual steps can be easily visualized, thus enabling interpretability of the method. We can therefore provide interesting insights into how our method refines and denoises disparity maps. The efficiency of our method is demonstrated by the publicly available stereo benchmarks Middlebury 2014 and Kitti 2015.