PANORAMA: The Rise of Omnidirectional Vision in the Embodied AI Era
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909791283052544 |
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| author | Zheng, Xu Liao, Chenfei Weng, Ziqiao Lei, Kaiyu Dongfang, Zihao He, Haocong Lyu, Yuanhuiyi Jiang, Lutao Qi, Lu Chen, Li Paudel, Danda Pani Yang, Kailun Zhang, Linfeng Van Gool, Luc Hu, Xuming |
| author_facet | Zheng, Xu Liao, Chenfei Weng, Ziqiao Lei, Kaiyu Dongfang, Zihao He, Haocong Lyu, Yuanhuiyi Jiang, Lutao Qi, Lu Chen, Li Paudel, Danda Pani Yang, Kailun Zhang, Linfeng Van Gool, Luc Hu, Xuming |
| contents | Omnidirectional vision, using 360-degree vision to understand the environment, has become increasingly critical across domains like robotics, industrial inspection, and environmental monitoring. Compared to traditional pinhole vision, omnidirectional vision provides holistic environmental awareness, significantly enhancing the completeness of scene perception and the reliability of decision-making. However, foundational research in this area has historically lagged behind traditional pinhole vision. This talk presents an emerging trend in the embodied AI era: the rapid development of omnidirectional vision, driven by growing industrial demand and academic interest. We highlight recent breakthroughs in omnidirectional generation, omnidirectional perception, omnidirectional understanding, and related datasets. Drawing on insights from both academia and industry, we propose an ideal panoramic system architecture in the embodied AI era, PANORAMA, which consists of four key subsystems. Moreover, we offer in-depth opinions related to emerging trends and cross-community impacts at the intersection of panoramic vision and embodied AI, along with the future roadmap and open challenges. This overview synthesizes state-of-the-art advancements and outlines challenges and opportunities for future research in building robust, general-purpose omnidirectional AI systems in the embodied AI era. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12989 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PANORAMA: The Rise of Omnidirectional Vision in the Embodied AI Era Zheng, Xu Liao, Chenfei Weng, Ziqiao Lei, Kaiyu Dongfang, Zihao He, Haocong Lyu, Yuanhuiyi Jiang, Lutao Qi, Lu Chen, Li Paudel, Danda Pani Yang, Kailun Zhang, Linfeng Van Gool, Luc Hu, Xuming Computer Vision and Pattern Recognition Omnidirectional vision, using 360-degree vision to understand the environment, has become increasingly critical across domains like robotics, industrial inspection, and environmental monitoring. Compared to traditional pinhole vision, omnidirectional vision provides holistic environmental awareness, significantly enhancing the completeness of scene perception and the reliability of decision-making. However, foundational research in this area has historically lagged behind traditional pinhole vision. This talk presents an emerging trend in the embodied AI era: the rapid development of omnidirectional vision, driven by growing industrial demand and academic interest. We highlight recent breakthroughs in omnidirectional generation, omnidirectional perception, omnidirectional understanding, and related datasets. Drawing on insights from both academia and industry, we propose an ideal panoramic system architecture in the embodied AI era, PANORAMA, which consists of four key subsystems. Moreover, we offer in-depth opinions related to emerging trends and cross-community impacts at the intersection of panoramic vision and embodied AI, along with the future roadmap and open challenges. This overview synthesizes state-of-the-art advancements and outlines challenges and opportunities for future research in building robust, general-purpose omnidirectional AI systems in the embodied AI era. |
| title | PANORAMA: The Rise of Omnidirectional Vision in the Embodied AI Era |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.12989 |