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\section{Conclusion} 
\label{sec:conclusion}
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Surround-view panoramic imaging enables rapid and comprehensive scene capture and is increasingly supported by both consumer and professional devices. In this work, we have reviewed recent advances in deep learning methods for transforming indoor panoramas into representations that support structural and geometric recovery, dynamic exploration, and editing, with particular emphasis on single-image approaches, a use case having vast practical importance and posing great visual computing challenges.

Since the field is evolving at a rapid pace, a complete survey is beyond reach, and our goal has been to organize current and emerging directions, thereby providing reasoned and accessible entry points for both researchers and practitioners. The report highlights that significant progress has already been achieved across analysis and synthesis, with many solutions transitioning into real-world applications. Nonetheless, substantial challenges remain, and we hope this overview will serve as a foundation and catalyst for further innovation in panoramic visual computing.



