# SynthmanticLiDAR: A Synthetic Dataset for Semantic Segmentation on LiDAR Imaging

Source: [SynthmanticLiDAR: A Synthetic Dataset for Semantic Segmentation on LiDAR Imaging](<https://www.sota2.com/research/paper/68fa76c41e209131cea6ee20>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:01:17.446Z

Authors: Javier Montalvo, Pablo Carballeira, \\'Alvaro Garc\\'ia-Mart\\'in

Published: 2025-01-31T11:09:10.000Z

## Abstract

Semantic segmentation on LiDAR imaging is increasingly gaining attention, as it can provide useful knowledge for perception systems and potential for autonomous driving. However, collecting and labeling real LiDAR data is an expensive and time-consuming task. While datasets such as SemanticKITTI have been manually collected and labeled, the introduction of simulation tools such as CARLA, has enabled the creation of synthetic datasets on demand.   In this work, we present a modified CARLA simulator designed with LiDAR semantic segmentation in mind, with new classes, more consistent object labeling with their counterparts from real datasets such as SemanticKITTI, and the possibility to adjust the object class distribution. Using this tool, we have generated SynthmanticLiDAR, a synthetic dataset for semantic segmentation on LiDAR imaging, designed to be similar to SemanticKITTI, and we evaluate its contribution to the training process of different semantic segmentation algorithms by using a naive transfer learning approach. Our results show that incorporating SynthmanticLiDAR into the training process improves the overall performance of tested algorithms, proving the usefulness of our dataset, and therefore, our adapted CARLA simulator.   The dataset and simulator are available in https://github.com/vpulab/SynthmanticLiDAR.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.19035>)

[PDF](<https://arxiv.org/pdf/2501.19035.pdf>)