  In this paper, we introduce the InLUT3D point cloud dataset, a comprehensive
resource designed to advance the field of scene understanding in indoor
environments. The dataset covers diverse spaces within the W7 faculty buildings
of Lodz University of Technology, characterised by high-resolution laser-based
point clouds and manual labelling. Alongside the dataset, we propose metrics
and benchmarking guidelines essential for ensuring trustworthy and reproducible
results in algorithm evaluation. We anticipate that the introduction of the
InLUT3D dataset and its associated benchmarks will catalyse future advancements
in 3D scene understanding, facilitating methodological rigour and inspiring new
approaches in the field.
