Unsupervised Disparity Estimation for Light Field Videos

Light field (LF) videos contain not only the spatial-angular information but also the temporal information, which are useful for disparity estimation. The existing work on disparity estimation for LF videos relies on supervised training with disparity labels. To overcome this reliance, we develop an unsupervised disparity estimation framework for LF videos, which consists of a matching branch to perform feature matching and a refinement branch to refine the disparity maps. Our framework also includes a cross-feature fusion module with self-attention and cross-attention to fuse the multi-frame features, and a cost aggregation transformer with cross-depth self-attention blocks to explore the global depth dependencies. Moreover, we propose a left-right consistency strategy to estimate the occlusion regions for the input views and introduce a occlusion-aware photometric loss to solve the occlusion issue. Experimental results demonstrate that our method achieves superior performance compared to the existing supervised and unsupervised methods.