  Laboratory processes involving small volumes of solutions and active
ingredients are often performed manually due to challenges in automation, such
as high initial costs, semi-structured environments and protocol variability.
In this work, we develop a flexible and cost-effective approach to address this
gap by introducing a vision-based system for liquid volume estimation and a
simulation-driven pouring method particularly designed for containers with
small openings. We evaluate both components individually, followed by an
applied real-world integration of cell culture automation using a UR5 robotic
arm. Our work is fully reproducible: we share our code at at
\url{https://github.com/DaniSchober/LabLiquidVision} and the newly introduced
dataset LabLiquidVolume is available at
https://data.dtu.dk/articles/dataset/LabLiquidVision/25103102.
