  Light field cameras and multi-camera arrays have emerged as promising
solutions for accurately estimating depth by passively capturing light
information. This is possible because the 3D information of a scene is embedded
in the 4D light field geometry. Commonly, depth estimation methods extract this
information relying on gradient information, heuristic-based optimisation
models, or learning-based approaches. This paper focuses mainly on explicitly
understanding and exploiting 4D geometrical cues for light field depth
estimation. Thus, a novel method is proposed, based on a non-learning-based
optimisation approach for depth estimation that explicitly considers surface
normal accuracy and occlusion regions by utilising a fully explainable 4D
geometric model of the light field. The 4D model performs depth/disparity
estimation by determining the orientations and analysing the intersections of
key 2D planes in 4D space, which are the images of 3D-space points in the 4D
light field. Experimental results show that the proposed method outperforms
both learning-based and non-learning-based state-of-the-art methods in terms of
surface normal angle accuracy, achieving a Median Angle Error on planar
surfaces, on average, 26.3\% lower than the state-of-the-art, and still being
competitive with state-of-the-art methods in terms of Mean Squared Error
$\vc{\times}$ 100 and Badpix 0.07.
