Enhancing the performance of light field camera by pattern projection

For industrial three-dimensional (3D)-measuring applications as well as for other

applications such as driver assistance systems in vehicles, a high-quality reproduction in a depth

map calculation is of paramount importance. Yet to date, light field cameras still exhibit severe

problems in imaging homogeneous surfaces such as smooth metallic surfaces, plastics, or semi-

conductor materials. On those, the necessary contrast for determining the topography is not

present or too weak. Here, we present an innovative approach to improve the performance

of light field cameras (also called plenoptic cameras) for various applications in 3D measurement

technology. More specifically, we show that this disadvantage can be avoided using structured

lighting pattern and analyze various lighting patterns (with respect to geometry, size, regularity,

etc.) used to improve the performance of the algorithm in greater detail. In addition, different

ways of projecting the pattern have been examined with regard to their advantages and disad-

vantages. We perform a systematic investigation of the impact of structured light illumination

with regard to different object materials, object geometries, and illumination patterns as well as

illumination sources. These examinations further comprise the analysis of environmental

influences and magnitude of measurement deviations in a light field camera measuring set-

up. Summing up, we investigated experimentally the influence of structured illumination on the

performance of 3D depth information without the need of additional phase information.

Silvan Ammann, Gilson Orlando, Joerg Pierer, Carsten Ziolek, Stefan J. Rinner, 2021
Zeitschrift / Sammelband:
Opt. Eng. 60(3), 034113 (2021), doi: 10.1117/1.OE.60.3.034113.