PhD Thesis: Deep Learning-Based Depth Estimation from Light Fields

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: UPR, ESE, and 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.

Citation

@phdthesis{leistner2024deep,
    title={Deep Learning-Based Depth Estimation from Light Fields},
    author={Leistner, Titus},
    year={2024}
}