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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.15676 |
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| _version_ | 1866911739866513408 |
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| author | Lei, Yinjie Wang, Zixuan Chen, Feng Wang, Guoqing Wang, Peng Yang, Yang |
| author_facet | Lei, Yinjie Wang, Zixuan Chen, Feng Wang, Guoqing Wang, Peng Yang, Yang |
| contents | Multi-modal 3D Intelligence has gained considerable attention due to its wide applications in autonomous driving and world simulation, etc. Compared to conventional single-modal 3D understanding, introducing an additional modality not only elevates the richness and precision of scene interpretation but also provides a foundation for higher-level physical world interaction. This becomes especially crucial in varied and challenging environments where solely relying on 3D data might be inadequate. While there has been a surge in the development of multi-modal 3D methods over the past six years, especially those integrating multi-camera images (3D+2D) and textual descriptions (3D+language), a comprehensive and in-depth review is notably absent. In this paper, we present a systematic survey of recent progress to bridge this gap. We begin by briefly summarizing the unique challenges among various 3D multi-modal tasks. After that, we present a novel taxonomy that delivers a thorough categorization of existing methods according to modalities and tasks, exploring their respective strengths and limitations. Furthermore, comparative results of recent approaches on several benchmark datasets, together with insightful analysis, are offered. Finally, we discuss the unresolved issues and provide several potential avenues for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_15676 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Recent Advances in Multi-modal 3D Intelligence: A Comprehensive Survey and Evaluation Lei, Yinjie Wang, Zixuan Chen, Feng Wang, Guoqing Wang, Peng Yang, Yang Computer Vision and Pattern Recognition Artificial Intelligence Multi-modal 3D Intelligence has gained considerable attention due to its wide applications in autonomous driving and world simulation, etc. Compared to conventional single-modal 3D understanding, introducing an additional modality not only elevates the richness and precision of scene interpretation but also provides a foundation for higher-level physical world interaction. This becomes especially crucial in varied and challenging environments where solely relying on 3D data might be inadequate. While there has been a surge in the development of multi-modal 3D methods over the past six years, especially those integrating multi-camera images (3D+2D) and textual descriptions (3D+language), a comprehensive and in-depth review is notably absent. In this paper, we present a systematic survey of recent progress to bridge this gap. We begin by briefly summarizing the unique challenges among various 3D multi-modal tasks. After that, we present a novel taxonomy that delivers a thorough categorization of existing methods according to modalities and tasks, exploring their respective strengths and limitations. Furthermore, comparative results of recent approaches on several benchmark datasets, together with insightful analysis, are offered. Finally, we discuss the unresolved issues and provide several potential avenues for future research. |
| title | Recent Advances in Multi-modal 3D Intelligence: A Comprehensive Survey and Evaluation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2310.15676 |