FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data
Open accessAbstract
We introduce a simple yet effective fusion method of LiDAR and RGB data to segment LiDAR point clouds. Utilizing the dense native range representation of a LiDAR sensor and the setup calibration, we establish point correspondences between the two input modalities. Subsequently, we are able to warp and fuse the features from one domain into the other. Therefore, we can jointly exploit information from both data sources within one single network. To show the merit of our method, we extend SqueezeSeg, a point cloud segmentation network, with an RGB feature branch and fuse it into the original structure. Our extension called FuseSeg leads to an improvement of up to 18% IoU on the KITTI benchmark. In addition to the improved accuracy, we also achieve real-time performance at 50 fps, five times as fast as the recording speed of the KITTI LiDAR data.
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Most recent approaches try to get the best of the three modalities by performing multiple fusion operations at different stages of the deep network.
“However, most recent approaches try to get the best of the three modalities by performing multiple fusion operations at different stages of the deep network [98,115,118].”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [118]
FuseSeg-LiDAR, using a SqueezeNet backbone, achieves an mIoU of 52.1 on the KITTI dataset for 2D + 3D semantic segmentation.
“| FuseSeg-LiDAR [118] | SqueezeNet [124] | 52.1 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [118]
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Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation
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- Authors
- Krispel, G., Opitz, M., Waltner, G., Possegger, H., & Bischof, H.
- Affiliations
- Graz University of Technology (AT)
- Venue
- 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2020-03
- Pages
- 1863-1872
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W2996158460
- Cited by
- 99
- References
- 40
- Citations by year
- 2026: 11 · 2025: 18 · 2024: 27 · 2023: 16 · 2022: 18 · 2021: 6 · 2020: 3
- FWCI
- 3.31
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Winter Conference on Applications of Computer Vision (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Österreichische Forschungsförderungsgesellschaft
- 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
- LiDAR point cloud segmentation, multimodal fusion, RGB-LiDAR fusion, point matching, feature warping, KITTI dataset
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