Superpixel-Based Optimization for Point Cloud Reconstruction from Light Field

Abstract

As one of the most popular immersive data formats, point cloud has attracted attention due to its flexibility and simplicity. The advantage of multi-view in light field can capture rich scene information and be effectively applied to 3D point cloud reconstruction. However, existing point cloud reconstruction methods based on light field depth estimation often cause outlier points at the edges of objects, which greatly disturbs the visual effects. In this paper, we propose a superpixel-based optimization scheme for point cloud, which is reconstructed from light field. We use the superpixel-based edge detection algorithm and designed joint bilateral filter to optimize the blurred edge of the depth map, which is obtained from the EPI-based depth estimation. Then, minimal residual outlier points are removed by statistical outlier filter after point cloud generating. Experimental results show that the proposed method increases at least 25.8% in level of details (LoD) compared with several state-of-the-art methods for the real-world and synthetic light field datasets. Besides, the proposed method can restore outlier points reliably and retain the sharp features of point cloud.