Schmähling, T.; Müller, T.; Eberhardt, J.; Elser, S. Simultaneous Stereo Matching and Confidence Estimation Network. Preprints2024, 2024070313. https://doi.org/10.20944/preprints202407.0313.v1
APA Style
Schmähling, T., Müller, T., Eberhardt, J., & Elser, S. (2024). Simultaneous Stereo Matching and Confidence Estimation Network. Preprints. https://doi.org/10.20944/preprints202407.0313.v1
Chicago/Turabian Style
Schmähling, T., Jörg Eberhardt and Stefan Elser. 2024 "Simultaneous Stereo Matching and Confidence Estimation Network" Preprints. https://doi.org/10.20944/preprints202407.0313.v1
Abstract
In this paper, we present a multi-task model that predicts disparities and confidence levels in deep stereo matching simultaneously. We do this by combining its successful model for each separate task and obtaining a multitask model that can be trained with a proposed loss function. We show the advantages of this model compared to training and predicting disparity and confidence sequentially. This method enables an improvement of 15% to 30% in the area under the curve (AUC) metric when trained parallel rather than sequential. In addition, the effect of weighting the components in the loss function on the stereo and confidence performance is investigated. By improving the confidence estimate, the practicality of stereo estimators for creating distance images is increased.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
