๐๐ข๐ณ๐ณ๐ฐ๐ธ ๐ท๐ช๐ฆ๐ธ๐ด ๐ญ๐ช๐ฎ๐ช๐ต ๐ธ๐ฐ๐ณ๐ญ๐ฅ ๐ฎ๐ฐ๐ฅ๐ฆ๐ญ๐ช๐ฏ๐จ.
[๐ญ/๐ญ๐ฌ] We are excited to introduce our ๐บ๐ฐ๐ฎ๐ฎ๐น๐จ๐ท๐ฏ 2026 work ๐๐ฆ๐ง๐ข๐๐จ๐๐ฆ โ a framework for long-horizon panoramic video generation ๐
It enables controllable scene wandering from a single image or short video.
Project Page: https://t.co/eaQPdrX6MJ
Github: https://t.co/uLY9SEagte
arXiv: https://t.co/T3WRxOylbk
@AdobeResearch
๐ขIntroducing 360Anything, our method for lifting any perspective image or video to gravity-aligned 360ยฐ panoramas without using any camera or 3D information. This enables consistent novel view synthesis and 3D scene reconstruction.
Project page: https://t.co/qTOEip0Jw2
๐งต
Welcome to the LowLevel CV platform!
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This platform focuses on low-level computer vision research, continuously integrating community resources such as papers, datasets, competitions, and workshops, and updating recruitment information from institutions.
Insta360 Research unveils Depth Any Panoramas
A new foundation model for panoramic depth estimation, excelling across diverse scenes and distances. It achieves robust, metrically consistent predictions in real-world scenarios.
Insta360 Research unveils Depth Any Panoramas
A new foundation model for panoramic depth estimation, excelling across diverse scenes and distances. It achieves robust, metrically consistent predictions in real-world scenarios.
Depth Any Panoramas!
DAP is an open, real-worldโgeneralized metric depth model, providing a strong foundation for spatial understanding in AIGC and real-world modeling.
๐ https://t.co/4xHG3y5wlS
๐ https://t.co/CYLkaoWi0P
๐ฎ https://t.co/BHk1itcjOq
Thanks @_akhaliq for sharing our work! The code and demo will be public soon. Please have a look if you are interested.๐ฅฐ๐ฅฐ๐ฅฐ
Paper: https://t.co/rywt2tPEKz, https://t.co/2b4lEhKk05
Github: https://t.co/0i6CIqpxLC
Project page: https://t.co/z7tL4nBWjn
๐ World model? Virtual embodied worlds? Immersive experiences?
Panoramic vision unlocks infinite possibilities! ๐
We just released our new survey๐ฅ:
โOne Flight Over the Gap: A Survey from Perspective to Panoramic Visionโ ๐๐ท
๐ Project page: https://t.co/I3qu2To7HA
2. Covering 20+ representative tasks (super-resolution, segmentation, generation, multimodal, etc.), we summarize key strategies and insights.
We hope this work provides a clear knowledge map while advancing panoramic vision in world models, VR, autonomous driving, and robotics.
1. We systematically review 300+ studies on panoramic vision, from imaging systems and projections to cross-method/task analyses and future trends. We highlight three challenges in perspective to panorama adaptation:
Geometric distortion
Non-uniform sampling
Boundary continuity