# An Evaluation of RGB and LiDAR Fusion for Semantic Segmentation

Source: [An Evaluation of RGB and LiDAR Fusion for Semantic Segmentation](<https://www.sota2.com/research/paper/68fa6a8d1e209131cea28be8>)

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

Data updated: 2025-10-25T23:27:27.413Z

Authors: Amr S. Mohamed, Ali Abdelkader, Mohamed Anany, Omar El-Behady, Muhammad Faisal, Asser Hangal, Hesham M. Eraqi, Mohamed N. Moustafa

Published: 2021-08-17T14:46:15.000Z

## Abstract

LiDARs and cameras are the two main sensors that are planned to be included in many announced autonomous vehicles prototypes. Each of the two provides a unique form of data from a different perspective to the surrounding environment. In this paper, we explore and attempt to answer the question: is there an added benefit by fusing those two forms of data for the purpose of semantic segmentation within the context of autonomous driving? We also attempt to show at which level does said fusion prove to be the most useful. We evaluated our algorithms on the publicly available SemanticKITTI dataset. All fusion models show improvements over the base model, with the mid-level fusion showing the highest improvement of 2.7% in terms of mean Intersection over Union (mIoU) metric.

## 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/2108.07661>)

[PDF](<https://arxiv.org/pdf/2108.07661.pdf>)