Normal Grouping Density Separation (NGDS): A Novel Object-Driven Indoor Point Cloud Partition Method

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.

Building Component Detection on Unstructured 3D Indoor Point Clouds Using RANSAC-Based Region Growing

“…Since the adjacent rooms share an inner wall whose thickness is thinner than the vertical structural member, the existing method uses the LiDAR-oriented normal vector to minimize the error. The normal vector-based segmentation divides the under-segmented inner wall into two clusters that are included in different rooms respectively [26].…”