Version 1
: Received: 20 May 2019 / Approved: 22 May 2019 / Online: 22 May 2019 (08:58:09 CEST)
How to cite:
Kruzhilov, I.; Romanov, M.; Konushin, A. Double Refinement Network for Room Layout Estimation. Preprints2019, 2019050270. https://doi.org/10.20944/preprints201905.0270.v1
Kruzhilov, I.; Romanov, M.; Konushin, A. Double Refinement Network for Room Layout Estimation. Preprints 2019, 2019050270. https://doi.org/10.20944/preprints201905.0270.v1
Kruzhilov, I.; Romanov, M.; Konushin, A. Double Refinement Network for Room Layout Estimation. Preprints2019, 2019050270. https://doi.org/10.20944/preprints201905.0270.v1
APA Style
Kruzhilov, I., Romanov, M., & Konushin, A. (2019). Double Refinement Network for Room Layout Estimation. Preprints. https://doi.org/10.20944/preprints201905.0270.v1
Chicago/Turabian Style
Kruzhilov, I., Mikhail Romanov and Anton Konushin. 2019 "Double Refinement Network for Room Layout Estimation" Preprints. https://doi.org/10.20944/preprints201905.0270.v1
Abstract
Layout estimation is a challenge of segmenting a cluttered room image into floor, walls and ceiling. We applied Double refinement network proved to be efficient in the depth estimation to generate heat maps for room key points and edges. Our method is the first not using encoder-decoder architecture for the room layout estimation. ResNet50 was utilized as a backbone for the network instead of VGG16 commonly used for the task, allowing the network to be more compact and faster. We designed a special layout score function and layout ranking algorithm for key points and edges output. Our method achieved the lowest pixel and corner errors on the LSUN data set. The input image resolution is 224*224.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
