Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.09573 (cs)
[Submitted on 10 Mar 2026]
Title:More than the Sum: Panorama-Language Models for Adverse Omni-Scenes
Authors:Weijia Fan, Ruiping Liu, Jiale Wei, Yufan Chen, Junwei Zheng, Zichao Zeng, Jiaming Zhang, Qiufu Li, Linlin Shen, Rainer Stiefelhagen
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Abstract:Existing vision-language models (VLMs) are tailored for pinhole imagery, stitching multiple narrow field-of-view inputs to piece together a complete omni-scene understanding. Yet, such multi-view perception overlooks the holistic spatial and contextual relationships that a single panorama inherently preserves. In this work, we introduce the Panorama-Language Modeling (PLM)paradigm, a unified $360^\circ$ vision-language reasoning that is more than the sum of its pinhole counterparts. Besides, we present PanoVQA, a large-scale panoramic VQA dataset that involves adverse omni-scenes, enabling comprehensive reasoning under object occlusions and driving accidents. To establish a foundation for PLM, we develop a plug-and-play panoramic sparse attention module that allows existing pinhole-based VLMs to process equirectangular panoramas without retraining. Extensive experiments demonstrate that our PLM achieves superior robustness and holistic reasoning under challenging omni-scenes, yielding understanding greater than the sum of its narrow parts. Project page: this https URL.
| Comments: | |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.09573 [cs.CV] |
| (or arXiv:2603.09573v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09573
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View a PDF of the paper titled More than the Sum: Panorama-Language Models for Adverse Omni-Scenes, by Weijia Fan and 9 other authors
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