An analysis of stereo depth estimation utilizing attention mechanisms, self-supervised pose estimators & temporal predictions

Oğuzman, Utku
By the recent success of deep learning, real-world applications of stereo depth estimation algorithms attracted the interest of many researchers. Using the available datasets, synthetic or real-world, the researchers begin analyzing their ideas for practical applications. In this thesis, a thorough analysis is performed of such an aim. The state-of-the-art stereo depth estimation algorithms are tried to be improved by incorporating attention mechanisms to the current networks and better initialization strategies in time. For this purpose, different amounts of attention modules are applied to one of the most successful stereo depth estimator networks. The performance of the proposed attention-based neural networks that is trained with the synthetic stereo datasets under a supervised setting is compared against the performance of a baseline algorithm and it yielded superior results. When these neural networks are finetuned using a small annotated real-world dataset, the baseline algorithm had a better performance. Secondly, the temporal information available in the synthetic datasets is leveraged by teaching the proposed neural network how to initialize the current iteration by using the previous predictions. Finally, in order to finetune the neural network better for real-world use with the temporal information, a large unannotated real-world dataset is utilized under a self-supervised training setting using ego-pose estimation and optical flow networks. In general, it is observed that these settings yield better results against state-of-the-art methods in the synthetic-to-real world supervised training settings, and they are comparable after the finetuning operation.


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Citation Formats
U. Oğuzman, “An analysis of stereo depth estimation utilizing attention mechanisms, self-supervised pose estimators & temporal predictions,” M.S. - Master of Science, Middle East Technical University, 2022.