Shang Ta Yang, Chi-Han Peng, Peter Wonka, Hung Kuo Chu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


We present PanoAnnotator, a semi-automatic system that facilitates the annotation of 2D indoor panoramas to obtain high-quality 3D room layouts. Observing that fully-automatic methods are often restricted to a subset of indoor panoramas and generate room layouts with mediocre quality, we instead propose a hybrid method to recover high-quality room layouts by leveraging both automatic estimations and user edits. Specifically, our system first employs state-of-the-art methods to automatically extract 2D/3D features from input panorama, based on which an initial Manhattan world layout is estimated. Then, the user can further edit the layout structure via a set of intuitive operations, while the system will automatically refine the geometry according to the extracted features. The experimental results show that our automatic initialization outperforms a selected fully-automatic state-of-the-art method in producing room layouts with higher accuracy. In addition, our complete system reduces annotation time when comparing with a fully-manual tool for achieving the same high quality results.
Original languageEnglish (US)
Title of host publicationSIGGRAPH Asia 2018 Posters on - SA '18
PublisherAssociation for Computing Machinery (ACM)
ISBN (Print)9781450360630
StatePublished - Nov 30 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01


Dive into the research topics of 'PanoAnnotator'. Together they form a unique fingerprint.

Cite this