Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matching
This work addresses the need for interpretable uncertainty in stereo matching to enhance safety and reliability in applications like autonomous driving, though it is incremental as it builds on existing uncertainty estimation methods.
The paper tackles the problem of estimating and analyzing uncertainty in stereo matching by proposing a framework that separates data and model uncertainty using Bayes risk, achieving accurate and efficient uncertainty estimation without compromising disparity prediction accuracy on four benchmarks.
Stereo matching plays a crucial role in various applications, where understanding uncertainty can enhance both safety and reliability. Despite this, the estimation and analysis of uncertainty in stereo matching have been largely overlooked. Previous works struggle to separate it into data (aleatoric) and model (epistemic) components and often provide limited interpretations of uncertainty. This interpretability is essential, as it allows for a clearer understanding of the underlying sources of error, enhancing both prediction confidence and decision-making processes. In this paper, we propose a new uncertainty-aware stereo matching framework. We adopt Bayes risk as the measurement of uncertainty and use it to separately estimate data and model uncertainty. We systematically analyze data uncertainty based on the probabilistic distribution of disparity and efficiently estimate model uncertainty without repeated model training. Experiments are conducted on four stereo benchmarks, and the results demonstrate that our method can estimate uncertainty accurately and efficiently, without sacrificing the disparity prediction accuracy.