RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving
Abstract
LiDAR has become a standard sensor for autonomous driving applications as they provide highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or due to shadows where the performance of cameras is degraded. LiDAR perception is gradually becoming mature for algorithms including object detection and SLAM. However, semantic segmentation algorithm remains to be relatively less explored. Motivated by the fact that semantic segmentation is a mature algorithm on image data, we explore sensor fusion based 3D segmentation. Our main contribution is to convert the RGB image to a polar-grid mapping representation used for LiDAR and design early and mid-level fusion architectures. Additionally, we design a hybrid fusion architecture that combines both fusion algorithms. We evaluate our algorithm on KITTI dataset which provides segmentation annotation for cars, pedestrians and cyclists. We evaluate two state-of-the-art architectures namely SqueezeSeg and PointSeg and improve the mIoU score by 10% in both cases relative to the LiDAR only baseline.
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RaLF3D, using a SqueezeSeg backbone, achieves an mIoU of 37.8 on the KITTI dataset for 2D + 3D semantic segmentation.
“| RaLF3D [119] | SqueezeSeg [33] | 37.8 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [119]
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- Authors
- El Madawi, K., Rashed, H., El Sallab, A., Nasr, O., Kamel, H., & Yogamani, S.
- Affiliations
- Cairo University (EG)
- Valeo (Ireland) (IE)
- Venue
- 2019 IEEE Intelligent Transportation Systems Conference (ITSC)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 7-12
- OpenAlex
- W2990479855
- Cited by
- 116
- References
- 21
- Citations by year
- 2026: 9 · 2025: 19 · 2024: 26 · 2023: 27 · 2022: 17 · 2021: 13 · 2020: 5
- FWCI
- 2.85
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Intelligent Transportation Systems (ITSC) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- 3D semantic segmentation, LiDAR, RGB-LiDAR fusion, autonomous driving, sensor fusion architecture, hybrid fusion, point clouds, KITTI dataset
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