  Light field disparity estimation is an essential task in computer vision with
various applications. Although supervised learning-based methods have achieved
both higher accuracy and efficiency than traditional optimization-based
methods, the dependency on ground-truth disparity for training limits the
overall generalization performance not to say for real-world scenarios where
the ground-truth disparity is hard to capture. In this paper, we argue that
unsupervised methods can achieve comparable accuracy, but, more importantly,
much higher generalization capacity and efficiency than supervised methods.
Specifically, we present the Occlusion Pattern Aware Loss, named OPAL, which
successfully extracts and encodes the general occlusion patterns inherent in
the light field for loss calculation. OPAL enables: i) accurate and robust
estimation by effectively handling occlusions without using any ground-truth
information for training and ii) much efficient performance by significantly
reducing the network parameters required for accurate inference. Besides, a
transformer-based network and a refinement module are proposed for achieving
even more accurate results. Extensive experiments demonstrate our method not
only significantly improves the accuracy compared with the SOTA unsupervised
methods, but also possesses strong generalization capacity, even for real-world
data, compared with supervised methods. Our code will be made publicly
available.
