TY - GEN
T1 - PanoAnnotator
AU - Yang, Shang Ta
AU - Peng, Chi-Han
AU - Wonka, Peter
AU - Chu, Hung Kuo
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2018/11/30
Y1 - 2018/11/30
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/631574
UR - https://dl.acm.org/citation.cfm?doid=3283289.3283304
UR - http://www.scopus.com/inward/record.url?scp=85060180391&partnerID=8YFLogxK
U2 - 10.1145/3283289.3283304
DO - 10.1145/3283289.3283304
M3 - Conference contribution
SN - 9781450360630
BT - SIGGRAPH Asia 2018 Posters on - SA '18
PB - Association for Computing Machinery (ACM)
ER -