SliceNet: deep dense depth estimation from a single indoor panorama using a slice-based representation
Giovanni Pintore, Marco Agus, Eva Almansa, Jens Schneider, and Enrico Gobbetti
2021
Abstract
We introduce a novel deep neural network to estimate a depth map from a single monocular indoor panorama. The network directly works on the equirectangular projection, exploiting the properties of indoor 360-degree images. Starting from the fact that gravity plays an important role in the design and construction of man-made indoor scenes, we propose a compact representation of the scene into vertical slices of the sphere, and we exploit long- and short-term relationships among slices to recover the equirectangular depth map. Our design makes it possible to maintain high-resolution information in the extracted features even with a deep network. The experimental results demonstrate that our method outperforms current state-of-the-art solutions in prediction accuracy, particularly for real-world data.
Reference and download information
Giovanni Pintore, Marco Agus, Eva Almansa, Jens Schneider, and Enrico Gobbetti. SliceNet: deep dense depth estimation from a single indoor panorama using a slice-based representation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Pages 11531-11540, 2021. DOI: 10.1109/CVPR46437.2021.01137. Selected as oral presentation.
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Bibtex citation record
@InProceedings{Pintore:2021:SDD, author = {Giovanni Pintore and Marco Agus and Eva Almansa and Jens Schneider and Enrico Gobbetti}, title = {{SliceNet}: deep dense depth estimation from a single indoor panorama using a slice-based representation}, booktitle = {Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, pages = {11531--11540}, year = {2021}, abstract = { We introduce a novel deep neural network to estimate a depth map from a single monocular indoor panorama. The network directly works on the equirectangular projection, exploiting the properties of indoor 360-degree images. Starting from the fact that gravity plays an important role in the design and construction of man-made indoor scenes, we propose a compact representation of the scene into vertical slices of the sphere, and we exploit long- and short-term relationships among slices to recover the equirectangular depth map. Our design makes it possible to maintain high-resolution information in the extracted features even with a deep network. The experimental results demonstrate that our method outperforms current state-of-the-art solutions in prediction accuracy, particularly for real-world data. }, doi = {10.1109/CVPR46437.2021.01137}, note = {Selected as oral presentation}, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Pintore:2021:SDD'}, }
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