Robust structured light field depth estimation via spinning line operator

Light fields can simultaneously capture the intensity and direction of each light ray, offering enhanced information for depth estimation. Currently, accurately extracting the epipolar plane lines and handling occlusion are the tough problems of the epipolar plane image depth estimation for light field. Passive depth estimation algorithms only relying on texture information from reconstructed object surfaces struggle to reconstruct texture-free areas, and exhibit poor noise resilience and low accuracy. To address these problems, this paper introduces structured light projection to add accurate and stable texture information, thereby improving the measurement accuracy and robustness, and proposes an epipolar plane line extraction algorithm suitable for structured light field data. This paper designs a spinning line operator based on Structured Light Field characteristics, and uses the variance of the points on the epipolar plane line as the cost of measuring the extraction accuracy. It judges and handles the occlusion problem by gradually removing abnormal points to make the variance converge. This paper measures and reconstructs objects such as standard blocks, standard balls, and portraits, and achieves satisfactory reconstruction results.