Depth estimation within a multi-line-scan light-field framework

Abstract

We present algorithms for depth estimation from light-field data acquired by a multi-line-scan image acquisition system. During image acquisition a 3-D light field is generated over time, which consists of multiple views of the object observed from different viewing angles. This allows for the construction of so-called epipolar plane images (EPIs) and subsequent EPI-based depth estimation. We compare several approaches based on testing various slope hypotheses in the EPI domain, which can directly be related to depth. The considered methods used in hypothesis assessment, which belong to a broader class of block-matching algorithms, are modified sum of absolute differences (MSAD), normalized cross correlation (NCC), census transform (CT) and modified census transform (MCT). The methods are compared w.r.t. their qualitative results for depth estimation and are presented for artificial and real-world data.