InLUT3D: Challenging real indoor dataset for point cloud analysis
This provides a new dataset and benchmarks for researchers in 3D scene understanding, though it is incremental as it builds on existing indoor datasets.
The authors introduced the InLUT3D dataset, a high-resolution, manually labeled point cloud resource for indoor scene understanding, and proposed metrics and benchmarks to improve algorithm evaluation and reproducibility.
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.