OccCasNet: Occlusion-Aware Cascade Cost Volume for Light Field Depth Estimation

Abstract

Depth estimation using the Light Field (LF) technique is an essential task with a wide range of practical applications. While mainstream approaches based on multi-view stereo techniques can attain exceptional accuracy by creating finer cost volumes, they are resource-intensive, time-consuming, and often overlook occlusion during cost volume construction. To address these issues and strike a better balance between accuracy and efficiency, we propose an occlusion-aware cascade cost volume for LF depth (disparity) estimation. Our cascaded strategy reduces the sampling number while maintaining a constant sampling interval, enabling the construction of a finer cost volume. We also introduce occlusion maps to enhance accuracy in constructing the occlusion-aware cost volume. Specifically, we first generate a coarse disparity map through a coarse disparity estimation network. Then, we warp the sub-aperture images (SAIs) of adjacent views to the center view based on the coarse disparity map to generate occlusion maps for each SAI by photo-consistency constraints. Finally, we seamlessly incorporate occlusion maps into cascade cost volume to construct an occlusion-aware refined cost volume, allowing the refined disparity estimation network to yield a more precise disparity map. Extensive experiments demonstrate the effectiveness of our method. Compared with the state-of-the-art techniques, our method achieves a superior balance between accuracy and efficiency, ranking first in the Q25 metric on the HCI 4D benchmark.