OccCasNet: Occlusion-Aware Cascade Cost Volume for Light Field Depth Estimation
Wentao Chao, Fuqing Duan, Xuechun Wang, Yingqian Wang, Ke Lü, Guanghui Wang
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.