Accurate and fast micro lenses depth maps from a 3D point cloud in light field cameras
Rodrigo Ferreira, Nuno Gonçalves · 2016
Light field cameras capture a scene's multi-directional light field with one image, allowing the estimation of depth. In this paper, we introduce a fully automatic method for depth estimation from a single plenoptic image running a RANSAC-like algorithm for feature matching. The novelty about our method is the global method to back project correspondences found using photometric similarity to obtain a 3D virtual point cloud and different methods to build a depth map from the 3D point cloud generated. We use lenses with different focal-lengths in a multiple depth map refining phase, generating a dense depth map. Tests with simulations and real images are presented and compared with the state of the art, showing comparable accuracy for substantial less computational time.