JOINT ESTIMATION OF DEPTH AND ITS UNCERTAINTY FROM STEREO IMAGES USING BAYESIAN DEEP LEARNING
Keywords: Dense Matching, Depth Reconstruction, Uncertainty Quantification, Deep Learning, Bayesian Neural Network
Abstract. The necessity to identify errors in the context of image-based 3D reconstruction has motivated the development of various methods for the estimation of uncertainty associated with depth estimates in recent years. Most of these methods exclusively estimate aleatoric uncertainty, which describes stochastic effects. On the other hand, epistemic uncertainty, which accounts for simplifications or incorrect assumptions with respect to the formulated model hypothesis, is often neglected. However, to accurately quantify the uncertainty inherent in a process, it is necessary to consider all potential sources of uncertainty and to model their stochastic behaviour appropriately. To approach this objective, a holistic method to jointly estimate disparity and uncertainty is presented in this work, taking into account both aleatoric and epistemic uncertainty. For this purpose, the proposed method is based on a Bayesian Neural Network, which is trained with variational inference using a probabilistic loss formulation. To evaluate the performance of the method proposed, extensive experiments are carried out on three datasets considering real-world indoor and outdoor scenes. The results of these experiments demonstrate that the proposed method is able to estimate the uncertainty accurately, while showing a similar and for some scenarios improved depth estimation capability compared to the dense stereo matching approach used as deterministic baseline. Moreover, the evaluation reveals the importance of considering both, aleatoric and epistemic uncertainty, in order to achieve an accurate estimation of the overall uncertainty related to a depth estimate.