Point Cloud Reconstruction Optimization of Light Field Image based on Intra-class Distance

A single light field image, containing multiple views, can be utilized in conjunction with estimated depth map to generate three-dimensional (3D) point cloud. It is noteworthy that many depth estimation algorithms frequently produce depth maps plagued by issues such as holes and blurred edges. Many algorithms use global optimization directly to optimize depth maps without considering the effects of holes and blurred edges, resulting in the loss of the sharp features of the point cloud. Different from their methods, we consider both holes and blurry edges as outlier points. Then, we classify angular sampling images based on color consistency differences and employ interclass distances to detect outlier points. With the help of the outlier mask, we can obtain the depth confidence for each point. Combining with the depth confidence, we define a weighting coefficient to determine the optimization neighborhood of the outlier point. Finally, the point cloud generated by the depth map can be further optimized based on the outlier mask. Experimental results on synthetic and real datasets demonstrate the effectiveness of the proposed algorithm.