Fast response aggregation for depth estimation using light field camera

Abstract

Light field cameras have been recently shown to be very effective in applications such as multifocusing and 3D reconstruction. These cameras can provide depth cues from both defocus and correspondence in a single snapshot. In this paper, we present a fast response aggregation framework for depth estimation by jointly using defocus and correspondence cues. Different from existing approaches, we perform a fast gradient preserving filtering in a label domain, instead of in a depth domain, to efficiently compute a dense depth map. The proposed approach comprises of three steps: 1) constructing defocus and correspondence response volumes, 2) adaptively smoothing the two volumes and performing Winner-Takes-All label selections, and 3) post-processing by using nonlocal image guided averaging. With such a compact framework, currently best depth estimation results can be achieved. This compact framework is suitable for various applications such as object segmentation and surface reconstruction.