Semi-Supervised Semantic Segmentation for Light Field Images Using Disparity Information.

Light field (LF) images enable numerous applications due to their ability to capture information for multiple views. Semantic segmentation is an essential task for LF scene understanding. However, existing supervised methods heavily rely on a large number of pixel-wise annotations. To relieve this problem, we propose a semi-supervised LF semantic segmentation method that requires only a small subset of labeled data and harnesses the LF disparity information. First, we design an unsupervised disparity estimation network, which can determine the disparity map for every view. With the estimated disparity maps, we generate pseudo-labels along with their weight maps for the peripheral views when only the labels of central views are available. We then merge the predictions from multiple views to obtain more reliable pseudo-labels for unlabeled data, and introduce a disparity-semantics consistency loss to enforce structure similarity. Moreover, we develop a comprehensive contrastive learning scheme that includes a pixel-level strategy to enhance feature representations and an object-level strategy to improve segmentation for individual objects. Our method demonstrates state-of-the-art performance on the benchmark LF semantic segmentation dataset under a variety of training settings and achieves comparable performance to supervised methods when trained under 1/2 protocol.