# An Indoor Radio Mapping Dataset Combining 3D Point Clouds and RSSI

arXiv: https://arxiv.org/abs/2511.00494 · HTML: https://mlsanity.com/paper/2511.00494/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 1 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024)
- [Radio Map Estimation -- An Open Dataset with Directive Transmitter Antennas and Initial Experiments](https://mlsanity.com/paper/2402.00878/) (2024)
- [ACT-GAN: Radio map construction based on generative adversarial networks with ACT blocks](https://mlsanity.com/paper/2401.08976/) (2024)
- [Evaluation and comparison of eight popular Lidar and Visual SLAM algorithms](https://mlsanity.com/paper/2208.02063/) (2022)
- [Data-Driven Radio Propagation Modeling using Graph Neural Networks](https://mlsanity.com/paper/2501.06236/) (2025)

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

- [Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction](https://mlsanity.com/paper/2610.19015/) (2026)

## Abstract (arXiv)

The growing number of smart devices supporting bandwidth-intensive and latency-sensitive applications, such as real-time video analytics, smart sensing, Extended Reality (XR), etc., necessitates reliable wireless connectivity in indoor environments. In such environments, accurate design of Radio Environment Maps (REMs) enables adaptive wireless network planning and optimization of Access Point (AP) placement. However, generating realistic REMs remains difficult due to the variability of indoor environments and the limitations of existing modeling approaches, which often rely on simplified layouts or fully synthetic data. These challenges are further amplified by the adoption of next-generation Wi-Fi standards, which operate at higher frequencies and suffer from limited range and wall penetration. To support the efforts in addressing these challenges, we collected a dataset that combines high-resolution 3D LiDAR scans with Wi-Fi RSSI measurements collected across 20 setups in a multi-room indoor environment. The dataset includes two measurement scenarios, the first without human presence in the environment, and the second with human presence, enabling the development and validation of REM estimation models that incorporate physical geometry and environmental dynamics. The described dataset supports research in data-driven wireless modeling and the development of high-capacity indoor communication networks.