Image Quality Assessment for Embodied AI
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914091832967168 |
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| author | Li, Chunyi Xiao, Jiaohao Zhang, Jianbo Wen, Farong Zhang, Zicheng Tian, Yuan Zhu, Xiangyang Liu, Xiaohong Cheng, Zhengxue Lin, Weisi Zhai, Guangtao |
| author_facet | Li, Chunyi Xiao, Jiaohao Zhang, Jianbo Wen, Farong Zhang, Zicheng Tian, Yuan Zhu, Xiangyang Liu, Xiaohong Cheng, Zhengxue Lin, Weisi Zhai, Guangtao |
| contents | Embodied AI has developed rapidly in recent years, but it is still mainly deployed in laboratories, with various distortions in the Real-world limiting its application. Traditionally, Image Quality Assessment (IQA) methods are applied to predict human preferences for distorted images; however, there is no IQA method to assess the usability of an image in embodied tasks, namely, the perceptual quality for robots. To provide accurate and reliable quality indicators for future embodied scenarios, we first propose the topic: IQA for Embodied AI. Specifically, we (1) based on the Mertonian system and meta-cognitive theory, constructed a perception-cognition-decision-execution pipeline and defined a comprehensive subjective score collection process; (2) established the Embodied-IQA database, containing over 36k reference/distorted image pairs, with more than 5m fine-grained annotations provided by Vision Language Models/Vision Language Action-models/Real-world robots; (3) trained and validated the performance of mainstream IQA methods on Embodied-IQA, demonstrating the need to develop more accurate quality indicators for Embodied AI. We sincerely hope that through evaluation, we can promote the application of Embodied AI under complex distortions in the Real-world. Project page: https://github.com/lcysyzxdxc/EmbodiedIQA |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16815 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Image Quality Assessment for Embodied AI Li, Chunyi Xiao, Jiaohao Zhang, Jianbo Wen, Farong Zhang, Zicheng Tian, Yuan Zhu, Xiangyang Liu, Xiaohong Cheng, Zhengxue Lin, Weisi Zhai, Guangtao Computer Vision and Pattern Recognition Robotics Embodied AI has developed rapidly in recent years, but it is still mainly deployed in laboratories, with various distortions in the Real-world limiting its application. Traditionally, Image Quality Assessment (IQA) methods are applied to predict human preferences for distorted images; however, there is no IQA method to assess the usability of an image in embodied tasks, namely, the perceptual quality for robots. To provide accurate and reliable quality indicators for future embodied scenarios, we first propose the topic: IQA for Embodied AI. Specifically, we (1) based on the Mertonian system and meta-cognitive theory, constructed a perception-cognition-decision-execution pipeline and defined a comprehensive subjective score collection process; (2) established the Embodied-IQA database, containing over 36k reference/distorted image pairs, with more than 5m fine-grained annotations provided by Vision Language Models/Vision Language Action-models/Real-world robots; (3) trained and validated the performance of mainstream IQA methods on Embodied-IQA, demonstrating the need to develop more accurate quality indicators for Embodied AI. We sincerely hope that through evaluation, we can promote the application of Embodied AI under complex distortions in the Real-world. Project page: https://github.com/lcysyzxdxc/EmbodiedIQA |
| title | Image Quality Assessment for Embodied AI |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2505.16815 |