# InLUT3D: Challenging real indoor dataset for point cloud analysis

arXiv: https://arxiv.org/abs/2408.03338 · HTML: https://mlsanity.com/paper/2408.03338/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 16 of 16)

- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Point Transformer V3: Simpler, Faster, Stronger](https://mlsanity.com/paper/2312.10035/) (2023)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [Joint 2D-3D-Semantic Data for Indoor Scene Understanding](https://mlsanity.com/paper/1702.01105/) (2017)
- [Mask3D: Mask Transformer for 3D Semantic Instance Segmentation](https://mlsanity.com/paper/2210.03105/) (2022)
- [Semantic3D.net: A new Large-scale Point Cloud Classification Benchmark](https://mlsanity.com/paper/1704.03847/) (2017)
- [OctFormer: Octree-based Transformers for 3D Point Clouds](https://mlsanity.com/paper/2305.03045/) (2023)
- [Vehicle Detection from 3D Lidar Using Fully Convolutional Network](https://mlsanity.com/paper/1608.07916/) (2016)
- [Real3D-AD: A Dataset of Point Cloud Anomaly Detection](https://mlsanity.com/paper/2309.13226/) (2023)
- [PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm](https://mlsanity.com/paper/2310.08586/) (2023)
- [Perceptual Quality Assessment of Colored 3D Point Clouds](https://mlsanity.com/paper/2111.05474/) (2021)
- [V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection](https://mlsanity.com/paper/2308.04409/) (2023)
- [TR3D: Towards Real-Time Indoor 3D Object Detection](https://mlsanity.com/paper/2302.02858/) (2023)
- [Building3D: An Urban-Scale Dataset and Benchmarks for Learning Roof Structures from Point Clouds](https://mlsanity.com/paper/2307.11914/) (2023)
- [Tinto: Multisensor Benchmark for 3D Hyperspectral Point Cloud Segmentation in the Geosciences](https://mlsanity.com/paper/2305.09928/) (2023)

## Built on by (most cited first, 1 of 1)

- [Through the Perspective of LiDAR: A Feature-Enriched and Uncertainty-Aware Annotation Pipeline for Terrestrial Point Cloud Segmentation](https://mlsanity.com/paper/2510.06582/) (2025)

## Abstract (arXiv)

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.