The DOI matches its Crossref record; 63 of the 65 references it cites were found in a registry or an index.
Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation
Abstract
3D LiDAR (light detection and ranging) semantic segmentation is important in scene understanding for many applications, such as auto-driving and robotics. For example, for autonomous cars equipped with RGB cameras and LiDAR, it is crucial to fuse complementary information from different sensors for robust and accurate segmentation. Existing fusion-based methods, however, may not achieve promising performance due to the vast difference between the two modalities. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds. To this end, we first project point clouds to the camera coordinates to provide spatio-depth information for RGB images. Then, we propose a two-stream network to extract features from the two modalities, separately, and fuse the features by effective residual-based fusion modules. Moreover, we propose additional perception-aware losses to measure the perceptual difference between the two modalities. Extensive experiments on two benchmark data sets show the superiority of our method. For example, on nuScenes, our PMF outperforms the state-of-the-art method by 0.8% in mIoU.
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“| PMF [107] | ResNet34 [16] | 63.9 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [107]
Citations
Cites
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CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation With Transformers
Zhang, J., Liu, H., Yang, K., Hu, X., Liu, R., & Stiefelhagen, R. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3300537 · Cited by 731
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
Yan, X., Gao, J., Zheng, C., Zheng, C., Zhang, R., Cui, S. R., & Li, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19815-1_39 · Cited by 301
Spherical Transformer for LiDAR-Based 3D Recognition
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Muhammad, K., Hussain, T., Ullah, H., Ser, J. D., Rezaei, M., Kumar, N., Hijji, M., Bellavista, P., & de Albuquerque, V. H. C. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3207665 · Cited by 232
Delivering Arbitrary-Modal Semantic Segmentation
Zhang, J., Liu, R., Shi, H., Yang, K., Reiß, S., Peng, K., Fu, H., Wang, K., & Stiefelhagen, R. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00116 · Cited by 215
Metadata
- Authors
- Zhuang, Z., Li, R., Jia, K., Wang, Q., Li, Y., & Tan, M.
- Affiliations
- South China University of Technology (CN)
- Shenzhen Youjia Innov Tech Co., Ltd
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 16260-16270
- OpenAlex
- W3173732446
- Cited by
- 216
- References
- 65
- Citations by year
- 2026: 26 · 2025: 58 · 2024: 68 · 2023: 47 · 2022: 16 · 2021: 1
- FWCI
- 10.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Funders
- Research and Development
- Ministry of Science and Technology
- Fundamental Research Funds for the Central Universities
- 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 › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Keywords
- multi-sensor fusion, RGB-LiDAR fusion, point cloud projection, two-stream network, autonomous driving, nuScenes
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