# Analysis of LiDAR Configurations on Off-road Semantic Segmentation Performance

arXiv: https://arxiv.org/abs/2306.16551 · HTML: https://mlsanity.com/paper/2306.16551/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 28 Jun 2023.

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

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation](https://mlsanity.com/paper/2011.10033/) (2020)
- [LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World](https://mlsanity.com/paper/2006.09348/) (2020)
- [SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instances](https://mlsanity.com/paper/2002.09147/) (2020)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [KITTI-CARLA: a KITTI-like dataset generated by CARLA Simulator](https://mlsanity.com/paper/2109.00892/) (2021)
- [ORFD: A Dataset and Benchmark for Off-Road Freespace Detection](https://mlsanity.com/paper/2206.09907/) (2022)
- [GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation](https://mlsanity.com/paper/1803.03254/) (2018)
- [LIBRE: The Multiple 3D LiDAR Dataset](https://mlsanity.com/paper/2003.06129/) (2020)
- [OFFSEG: A Semantic Segmentation Framework For Off-Road Driving](https://mlsanity.com/paper/2103.12417/) (2021)
- [Learn to Navigate Maplessly with Varied LiDAR Configurations: A Support Point-Based Approach](https://mlsanity.com/paper/2010.10209/) (2020)
- [LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud Segmentation](https://mlsanity.com/paper/2103.09160/) (2021)
- [Multi-view Incremental Segmentation of 3D Point Clouds for Mobile Robots](https://mlsanity.com/paper/1902.06768/) (2019)
- [An Optimal LiDAR Configuration Approach for Self-Driving Cars](https://mlsanity.com/paper/1805.07843/) (2018)

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

- [Off-Road LiDAR Intensity Based Semantic Segmentation](https://mlsanity.com/paper/2401.01439/) (2024)

## Abstract (arXiv)

This paper investigates the impact of LiDAR configuration shifts on the performance of 3D LiDAR point cloud semantic segmentation models, a topic not extensively studied before. We explore the effect of using different LiDAR channels when training and testing a 3D LiDAR point cloud semantic segmentation model, utilizing Cylinder3D for the experiments. A Cylinder3D model is trained and tested on simulated 3D LiDAR point cloud datasets created using the Mississippi State University Autonomous Vehicle Simulator (MAVS) and 32, 64 channel 3D LiDAR point clouds of the RELLIS-3D dataset collected in a real-world off-road environment. Our experimental results demonstrate that sensor and spatial domain shifts significantly impact the performance of LiDAR-based semantic segmentation models. In the absence of spatial domain changes between training and testing, models trained and tested on the same sensor type generally exhibited better performance. Moreover, higher-resolution sensors showed improved performance compared to those with lower-resolution ones. However, results varied when spatial domain changes were present. In some cases, the advantage of a sensor's higher resolution led to better performance both with and without sensor domain shifts. In other instances, the higher resolution resulted in overfitting within a specific domain, causing a lack of generalization capability and decreased performance when tested on data with different sensor configurations.