A deep learning method for frame selection in videos for structure from motion pipelines
Banterle F, Gong R, Corsini M, Ganovelli F, Van Gool L, Cignoni P
Time-frequency analysis Structure from motion Video Processing Video sequences Pipelines Computer architecture Streaming media Prediction algorithms Point-Cloud Generation Structure from Motion Deep Learning
Structure-from-Motion (SfM) using the frames of a video sequence can be a challenging task because there is a lot of redundant information, the computational time increases quadratically with the number of frames, there would be low-quality images (e.g., blurred frames) that can decrease the final quality of the reconstruction, etc. To overcome all these issues, we present a novel deep-learning architecture that is meant for speeding up SfM by selecting frames using predicted sub-sampling frequency. This architecture is general and can learn/distill the knowledge of any algorithm for selecting frames from a video for generating high-quality reconstructions. One key advantage is that we can run our architecture in real-time saving computations while keeping high-quality results.
Source: PROCEEDINGS - INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, pp. 3667-3671. Anchorage, Alaska, USA, 19-22/09/2021
Publisher: IEEE
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@inproceedings{oai:it.cnr:prodotti:465907,
title = {A deep learning method for frame selection in videos for structure from motion pipelines},
author = {Banterle F and Gong R and Corsini M and Ganovelli F and Van Gool L and Cignoni P},
publisher = {IEEE},
doi = {10.1109/icip42928.2021.9506227},
booktitle = {PROCEEDINGS - INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, pp. 3667-3671. Anchorage, Alaska, USA, 19-22/09/2021},
year = {2021}
}
0000-0002-6374-6657Bibliographic record
Deposited version
Deposited version
DOI
10.1109/icip42928.2021.9506227
