An Evaluation of RGB and LiDAR Fusion for Semantic Segmentation

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

arXiv · 2108.07661

An Evaluation of RGB and LiDAR Fusion for Semantic Segmentation

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

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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.

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Amr S. Mohamed, Ali Abdelkader, Mohamed Anany, Omar El-Behady, Muhammad Faisal, Asser Hangal, Hesham M. Eraqi, Mohamed N. Moustafa. 2021-08-17. An Evaluation of RGB and LiDAR Fusion for Semantic Segmentation. https://arxiv.org/abs/2108.07661

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