# Reflectivity Is All You Need!: Advancing LiDAR Semantic Segmentation

arXiv: https://arxiv.org/abs/2403.13188 · HTML: https://mlsanity.com/paper/2403.13188/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 19 Mar 2024.

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

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Point Transformer V3: Simpler, Faster, Stronger](https://mlsanity.com/paper/2312.10035/) (2023)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation](https://mlsanity.com/paper/2011.10033/) (2020)
- [SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving](https://mlsanity.com/paper/2003.03653/) (2020)
- [Rethinking Range View Representation for LiDAR Segmentation](https://mlsanity.com/paper/2303.05367/) (2023)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving](https://mlsanity.com/paper/1909.08291/) (2019)
- [CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving](https://mlsanity.com/paper/2207.12691/) (2022)
- [Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving](https://mlsanity.com/paper/2103.11351/) (2021)
- [Off-Road LiDAR Intensity Based Semantic Segmentation](https://mlsanity.com/paper/2401.01439/) (2024)

## Built on by (most cited first, 9 of 9)

- [LOGen: Toward Lidar Object Generation by Point Diffusion](https://mlsanity.com/paper/2412.07385/) (2024)
- [InvRGB+L: Inverse Rendering of Complex Scenes with Unified Color and LiDAR Reflectance Modeling](https://mlsanity.com/paper/2507.17613/) (2025)
- [Material-informed Gaussian Splatting for 3D World Reconstruction in a Digital Twin](https://mlsanity.com/paper/2511.20348/) (2025)
- [Efficient LiDAR Reflectance Compression via Scanning Serialization](https://mlsanity.com/paper/2505.09433/) (2025)
- [MinkUNeXt-SI: Improving point cloud-based place recognition including spherical coordinates and LiDAR intensity](https://mlsanity.com/paper/2505.17591/) (2025)
- [Physically Based Neural LiDAR Resimulation](https://mlsanity.com/paper/2507.12489/) (2025)
- [Methods for the Segmentation of Reticular Structures Using 3D LiDAR Data: A Comparative Evaluation](https://mlsanity.com/paper/2507.20589/) (2025)
- [VERIA: Verification-Centric Multimodal Instance Augmentation for Long-Tailed 3D Object Detection](https://mlsanity.com/paper/2603.24294/) (2026)
- [TravKAN: Fast and Interpretable Nonlinear Traversability Analysis with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2608.02320/) (2026)

## Abstract (arXiv)

LiDAR semantic segmentation frameworks predominantly use geometry-based features to differentiate objects within a scan. Although these methods excel in scenarios with clear boundaries and distinct shapes, their performance declines in environments where boundaries are indistinct, particularly in off-road contexts. To address this issue, recent advances in 3D segmentation algorithms have aimed to leverage raw LiDAR intensity readings to improve prediction precision. However, despite these advances, existing learning-based models face challenges in linking the complex interactions between raw intensity and variables such as distance, incidence angle, material reflectivity, and atmospheric conditions. Building upon our previous work, this paper explores the advantages of employing calibrated intensity (also referred to as reflectivity) within learning-based LiDAR semantic segmentation frameworks. We start by demonstrating that adding reflectivity as input enhances the LiDAR semantic segmentation model by providing a better data representation. Extensive experimentation with the Rellis-3d off-road dataset shows that replacing intensity with reflectivity results in a 4% improvement in mean Intersection over Union (mIoU) for off-road scenarios. We demonstrate the potential benefits of using calibrated intensity for semantic segmentation in urban environments (SemanticKITTI) and for cross-sensor domain adaptation. Additionally, we tested the Segment Anything Model (SAM) using reflectivity as input, resulting in improved segmentation masks for LiDAR images.