# Machine Learning for LiDAR-Based Indoor Surface Classification in Intelligent Wireless Environments

arXiv: https://arxiv.org/abs/2509.03813 · HTML: https://mlsanity.com/paper/2509.03813/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [Millimeter Wave Cellular Wireless Networks: Potentials and Challenges](https://mlsanity.com/paper/1401.2560/) (2014)
- [LIDAR Data for Deep Learning-Based mmWave Beam-Selection](https://mlsanity.com/paper/1908.07488/) (2019)
- [The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications](https://mlsanity.com/paper/2405.01816/) (2024)
- [Indoor Wireless Channel Properties at Millimeter Wave and Sub-Terahertz Frequencies](https://mlsanity.com/paper/1908.09765/) (2019)
- [LiDAR-Aided Millimeter-Wave Range Extension using a Passive Mirror Reflector](https://mlsanity.com/paper/2409.01608/) (2024)
- [Scattering Mechanisms and Modeling for Terahertz Wireless Communications](https://mlsanity.com/paper/1903.02657/) (2019)
- [Terahertz channel modeling based on surface sensing characteristics](https://mlsanity.com/paper/2404.02661/) (2024)
- [LiSu: A Dataset and Method for LiDAR Surface Normal Estimation](https://mlsanity.com/paper/2503.08601/) (2025)

## Abstract (arXiv)

Reliable connectivity in millimeter-wave (mmWave) and sub-terahertz (sub-THz) networks depends on reflections from surrounding surfaces, as high-frequency signals are highly vulnerable to blockage. The scattering behavior of a surface is determined not only by material permittivity but also by roughness, which governs whether energy remains in the specular direction or is diffusely scattered. This paper presents a LiDAR-driven machine learning framework for classifying indoor surfaces into semi-specular and low-specular categories, using optical reflectivity as a proxy for electromagnetic scattering behavior. A dataset of over 78,000 points from 15 representative indoor materials was collected and partitioned into 3 cm x 3 cm patches to enable classification from partial views. Patch-level features capturing geometry and intensity, including elevation angle, natural-log-scaled intensity, and max-to-mean ratio, were extracted and used to train Random Forest, XGBoost, and neural network classifiers. Results show that ensemble tree-based models consistently provide the best trade-off between accuracy and robustness, confirming that LiDAR-derived features capture roughness-induced scattering effects. The proposed framework enables the generation of scatter aware environment maps and digital twins, supporting adaptive beam management, blockage recovery, and environment-aware connectivity in next-generation networks.