Abstract: Light field depth estimation is an important scientific problem of light field processing and applications. However, the existing studies ignore the geometric occlusion relationship among views in the light field. By analyzing the occlusion among different views, an unsupervised light field depth estimation method based on sub-light field occlusion fusion is proposed. The proposed method first adopts an effective sub-light field division mechanism to consider the depth relationship at different angular positions. Specifically, the views on the primary and secondary diagonals of the light field sub-aperture arrays are divided into four sub-light fields, i.e., top-left, top-right, bottom-left, and bottom-right. Then, a spatial pyramid pooling feature extraction and a U-Net network are leveraged to estimate the depths of the sub-light fields. Finally, an occlusion fusion strategy is designed to fuse all sub-light field depths to obtain the final depth. This strategy assigns greater weights to the sub-light field depth with higher accuracy in the occlusion region, thus reducing the occlusion effect. In addition, a weighted spatial and an angular consistency loss are employed to constrain network training and enhance robustness. Experimental results demonstrate that the proposed method exhibits favorable performance in both quantitative metrics and qualitative comparisons.