Geometric Calibration of Micro-Lens-Based Light Field Cameras Using Line Features

TLDR

Accurate geometric calibration underpins many applications of light field cameras. The paper proposes a novel method for calibrating micro‑lens‑based light field cameras. The method uses raw images, extracts line features from selected micro‑lens regions, formulates a compact projection model, solves for intrinsic and extrinsic parameters linearly, and refines them with non‑linear optimization. Experiments show that the method accurately aligns rays with pixels in raw images.

Abstract

We present a novel method for the geometric calibration of micro-lens-based light field cameras. Accurate geometric calibration is the basis of various applications. Instead of using sub-aperture images, we directly utilize raw images for calibration. We select appropriate regions in raw images and extract line features from micro-lens images in those regions. For the entire process, we formulate a new projection model of a micro-lens-based light field camera, which contains a smaller number of parameters than previous models. The model is transformed into a linear form using line features. We compute the initial solution of both the intrinsic and the extrinsic parameters by a linear computation and refine them via non-linear optimization. Experimental results demonstrate the accuracy of the correspondences between rays and pixels in raw images, as estimated by the proposed method.