Entropy-difference based stereo error detection

TL;DR

This paper introduces a novel stereo error detection method based on entropy differences between input images and depth maps, which is independent of the stereo matching algorithm's characteristics.

Contribution

We propose an entropy-difference based confidence measure for stereo depth estimation that relies solely on input images and depth maps, improving error detection.

Findings

Outperforms 17 existing confidence measures in most aspects

Effective in identifying incorrect depth estimates

Demonstrated on Middlebury dataset with established metrics

Abstract

Stereo depth estimation is error-prone; hence, effective error detection methods are desirable. Most such existing methods depend on characteristics of the stereo matching cost curve, making them unduly dependent on functional details of the matching algorithm. As a remedy, we propose a novel error detection approach based solely on the input image and its depth map. Our assumption is that, entropy of any point on an image will be significantly higher than the entropy of its corresponding point on the image's depth map. In this paper, we propose a confidence measure, Entropy-Difference (ED) for stereo depth estimates and a binary classification method to identify incorrect depths. Experiments on the Middlebury dataset show the effectiveness of our method. Our proposed stereo confidence measure outperforms 17 existing measures in all aspects except occlusion detection. Established metrics such as precision, accuracy, recall, and area-under-curve are used to demonstrate the effectiveness of our method.