Attention-based matching cost for disparity estimation from light fields

This paper presents a neural network designed for light field (LF) disparity estimation. We improve the network's capability to use spatial and geometric information from light field data by:1. Incorporating positional encoding;2. Adding edge attention mechanisms. The positional encoding aids in deciphering the 3D structure of scenes, which is crucial for accurate LF disparity estimation. Meanwhile, edge attention directs the network to prioritize edge details, enabling the construction of more precise disparity maps. Additionally, edge attention ensures global consistency in disparity estimates, particularly in areas with prominent object edges and limited texture, where it can minimize estimation uncertainty. The attention mechanism also selectively refines features from each view, further boosting the accuracy of disparity estimation. Experiments demonstrate our model's improved accuracy, underscoring the effectiveness of our approach in enhancing LF disparity estimation techniques. The proposed method not only enhances performance but also streamlines the network architecture, making it more scalable and suitable for diverse computer vision scenarios.