The DOI matches its Crossref record; 141 of the 141 references it cites were found in a registry or an index.
Multi-Modal 3D Object Detection in Autonomous Driving: A Survey and Taxonomy
Abstract
Autonomous vehicles require constant environmental perception to obtain the distribution of obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional module as it can simultaneously predict surrounding objects' categories, locations, and sizes. Generally, autonomous vehicles are equipped with multiple sensors, including cameras and LiDARs. The fact that single-modal methods suffer from unsatisfactory detection performance motivates utilizing multiple modalities as inputs to compensate for single sensor faults. Although many multi-modal fusion detection algorithms exist, there is still a lack of comprehensive and in-depth analysis of these methods to clarify how to fuse multi-modal data effectively. Therefore, this paper surveys recent advancements in fusion detection methods. First, we present the broad background of multi-modal 3D object detection and identify the characteristics of widely used datasets along with their evaluation metrics. Second, instead of the traditional classification method of early, middle, and late fusion, we categorize and analyze all fusion methods from three aspects: feature representation, alignment, and fusion, which reveals how these fusion methods are implemented in an essential way. Third, we provide an in-depth comparison of their pros and cons and compare their performance in mainstream datasets. Finally, we further summarize current challenges and research trends for realizing the full potential of multi-modal 3D object detection.
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Cites
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Self-citationEnd-to-End Object Detection with Transformers
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End-to-End Object Detection with Transformers
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · arXiv · doi:10.48550/arxiv.2005.12872 · Cited by 18,253
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A survey on Image Data Augmentation for Deep Learning
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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nuScenes: A Multimodal Dataset for Autonomous Driving
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VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
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Cited by
Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving
Ren, S., Lei, Z., Wang, Z., Dianati, M., Wang, Y., Chen, S., & Zhang, W. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3371974 · Cited by 75
Towards Robust 3D Object Detection with LiDAR and 4D Radar Fusion in Various Weather Conditions
Chae, Y., Kim, H., & Yoon, K.-J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01436 · Cited by 49
Multi-Sem Fusion: Multimodal Semantic Fusion for 3-D Object Detection
Xu, S., Li, F., Song, Z., Fang, J., Wang, S., & Yang, Z.-X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3387732 · Cited by 26
Self-citation
Metadata
- Authors
- Wang, L., Zhang, X., Song, Z., Bi, J., Zhang, G., Wei, H., Tang, L., Yang, L., Li, J., Jia, C., & Zhao, L.
- Venue
- IEEE Transactions on Intelligent Vehicles
- Published
- 2023-07
- Volume
- 8
- Issue
- 7
- Pages
- 3781-3798
- ISSN
- 2379-8858 (ISSN-L)
- 2379-8904
- OpenAlex
- W4362654659
- Cited by
- 269
- References
- 141
- FWCI
- 19.54
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, L., Zhang, X., Song, Z., Bi, J., Zhang, G., Wei, H., Tang, L., Yang, L., Li, J., Jia, C., & Zhao, L. (2023). Multi-Modal 3D Object Detection in Autonomous Driving: A Survey and Taxonomy. IEEE Transactions on Intelligent Vehicles. https://doi.org/10.1109/tiv.2023.3264658
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