Stereo matching on epipolar plane image for light field depth estimation via oriented structure

Depth estimation plays a pivotal role in civil engineering such as road surface defect detection, as it serves as a valuable tool, offering high-precision and critical information about scene surface geometry. The Light Field captures both spatial and angular information of a scene, enabling precise depth estimation. The Epipolar Plane Image represents a specific 2-dimensional slice of the light field and is characterized by multiple depth-related lines. Previous epipolar plane image-based methods typically estimate depth maps by extracting the optimal slope for each line; however, they often neglect the visual relationships within this representation, leading to inaccuracies. In this paper, we explore the visual relationship of it and propose a novel visual feature, termed Oriented Structure, which can be utilized to compute scene depth. Similar to previous stereo matching-based methods, we design a new epipolar plane image-based cost volume to extract depth cues from this structure. The cost volume combines the occlusion robustness of epipolar plane image-based methods with the noise robustness of stereo matching-based methods, resulting in smoother depth maps with sharper edges. Building on the framework of existing stereo matching networks, we introduce an epipolar plane image-based stereo matching network for light field depth prediction. Finally, we conduct experiments using both synthetic and real datasets, demonstrating that our network produces higher-quality depth maps compared to previous state-of-the-art methods, ranking first (about 1.405 mean squared error) on the 4-dimensional light field benchmark. Additionally, we also apply our method to defect detection tasks, providing accurate depth information that leads to improved results.