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Abstract

Light fields have emerged as a highly accurate method for depth estimation, known for its precision and robustness against occlusions. After the decline of consumer based light field cameras, new industrial and research applications have emerged with very different demands, including the usage of high-resolution wide-baseline camera arrays and the need for a reliable confidence measure. This thesis responds to these evolving requirements with two main contributions: First, the introduction of EPI-Shift, a deep learning-based framework for depth estimation from both, small- and wide-baseline light fields. EPI-Shift combines discrete disparity classification with continuous disparity-offset regression and performs well on wide-baseline light fields, even when trained solely on narrow-baseline data. The second contribution focuses on multimodal posterior regression in depth estimation, useful for dealing with reflective and semi-transparent surfaces and for uncertainty quantification. This thesis contributes three deep learning-based approaches for depth posterior regression: Unimodal Posterior Regression (UPR), EPI-Shift Ensemble (ESE), and Discrete Posterior Prediction (DPP). Each of these methods displays strengths and weaknesses for different applications, evaluated using a novel multimodal light field depth dataset. Even with the extended applicability to wide-baseline light fields and the enhanced posterior regression capabilities, the performance of the presented methods stays on par with other state-of-the art approaches, marking a significant step towards practicality for today’s applications.