InLUT3D: Challenging real indoor dataset for point cloud analysis

TL;DR

The paper introduces InLUT3D, a detailed indoor point cloud dataset with benchmarks, aiming to improve scene understanding and evaluation consistency in 3D indoor environment analysis.

Contribution

It presents a new comprehensive indoor point cloud dataset with benchmarking metrics, fostering progress and reproducibility in 3D scene understanding research.

Findings

High-resolution laser-based point clouds of indoor environments

Manual labeling for accurate scene annotation

Benchmarking guidelines for algorithm evaluation

Abstract

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.