One Flight Over the Gap: A Survey from Perspective to Panoramic Vision

• URL: http://arxiv.org/abs/2509.04444v2
• Date: Tue, 09 Sep 2025 15:29:50 GMT
• Title: One Flight Over the Gap: A Survey from Perspective to Panoramic Vision
• Authors: Xin Lin, Xian Ge, Dizhe Zhang, Zhaoliang Wan, Xianshun Wang, Xiangtai Li, Wenjie Jiang, Bo Du, Dacheng Tao, Ming-Hsuan Yang, Lu Qi,
• Abstract summary: This survey reviews recent panoramic vision techniques with a particular emphasis on the perspective-to-panorama adaptation.<n>We first revisit the panoramic imaging pipeline and projection methods to build the prior knowledge required for analyzing the structural disparities.<n>Building on this, we cover 20+ representative tasks drawn from more than 300 research papers in two dimensions.
• Score: 117.80970697177025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driven by the demand for spatial intelligence and holistic scene perception, omnidirectional images (ODIs), which provide a complete 360\textdegree{} field of view, are receiving growing attention across diverse applications such as virtual reality, autonomous driving, and embodied robotics. Despite their unique characteristics, ODIs exhibit remarkable differences from perspective images in geometric projection, spatial distribution, and boundary continuity, making it challenging for direct domain adaption from perspective methods. This survey reviews recent panoramic vision techniques with a particular emphasis on the perspective-to-panorama adaptation. We first revisit the panoramic imaging pipeline and projection methods to build the prior knowledge required for analyzing the structural disparities. Then, we summarize three challenges of domain adaptation: severe geometric distortions near the poles, non-uniform sampling in Equirectangular Projection (ERP), and periodic boundary continuity. Building on this, we cover 20+ representative tasks drawn from more than 300 research papers in two dimensions. On one hand, we present a cross-method analysis of representative strategies for addressing panoramic specific challenges across different tasks. On the other hand, we conduct a cross-task comparison and classify panoramic vision into four major categories: visual quality enhancement and assessment, visual understanding, multimodal understanding, and visual generation. In addition, we discuss open challenges and future directions in data, models, and applications that will drive the advancement of panoramic vision research. We hope that our work can provide new insight and forward looking perspectives to advance the development of panoramic vision technologies. Our project page is https://insta360-research-team.github.io/Survey-of-Panorama

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