DynamicStereo: consistent dynamic depth from stereo videos

We consider the problem of reconstructing a dynamic scene observed from a stereo camera. Most existing methods for depth from stereo treat different stereo frames independently, leading to temporally inconsistent depth predictions. Temporal consistency is especially important for immersive AR or VR...

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Main Authors: Karaev, N, Rocco, I, Graham, B, Neverova, N, Vedaldi, A, Rupprecht, C
Format: Internet publication
Language:English
Published: 2023
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author Karaev, N
Rocco, I
Graham, B
Neverova, N
Vedaldi, A
Rupprecht, C
author_facet Karaev, N
Rocco, I
Graham, B
Neverova, N
Vedaldi, A
Rupprecht, C
author_sort Karaev, N
collection OXFORD
description We consider the problem of reconstructing a dynamic scene observed from a stereo camera. Most existing methods for depth from stereo treat different stereo frames independently, leading to temporally inconsistent depth predictions. Temporal consistency is especially important for immersive AR or VR scenarios, where flickering greatly diminishes the user experience. We propose DynamicStereo, a novel transformer-based architecture to estimate disparity for stereo videos. The network learns to pool information from neighboring frames to improve the temporal consistency of its predictions. Our architecture is designed to process stereo videos efficiently through divided attention layers. We also introduce Dynamic Replica, a new benchmark dataset containing synthetic videos of people and animals in scanned environments, which provides complementary training and evaluation data for dynamic stereo closer to real applications than existing datasets. Training with this dataset further improves the quality of predictions of our proposed DynamicStereo as well as prior methods. Finally, it acts as a benchmark for consistent stereo methods.
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spelling oxford-uuid:08ca62ed-4709-4ef7-9761-a8407a6cb4c92024-05-30T15:47:22ZDynamicStereo: consistent dynamic depth from stereo videosInternet publicationhttp://purl.org/coar/resource_type/c_7ad9uuid:08ca62ed-4709-4ef7-9761-a8407a6cb4c9EnglishSymplectic Elements2023Karaev, NRocco, IGraham, BNeverova, NVedaldi, ARupprecht, CWe consider the problem of reconstructing a dynamic scene observed from a stereo camera. Most existing methods for depth from stereo treat different stereo frames independently, leading to temporally inconsistent depth predictions. Temporal consistency is especially important for immersive AR or VR scenarios, where flickering greatly diminishes the user experience. We propose DynamicStereo, a novel transformer-based architecture to estimate disparity for stereo videos. The network learns to pool information from neighboring frames to improve the temporal consistency of its predictions. Our architecture is designed to process stereo videos efficiently through divided attention layers. We also introduce Dynamic Replica, a new benchmark dataset containing synthetic videos of people and animals in scanned environments, which provides complementary training and evaluation data for dynamic stereo closer to real applications than existing datasets. Training with this dataset further improves the quality of predictions of our proposed DynamicStereo as well as prior methods. Finally, it acts as a benchmark for consistent stereo methods.
spellingShingle Karaev, N
Rocco, I
Graham, B
Neverova, N
Vedaldi, A
Rupprecht, C
DynamicStereo: consistent dynamic depth from stereo videos
title DynamicStereo: consistent dynamic depth from stereo videos
title_full DynamicStereo: consistent dynamic depth from stereo videos
title_fullStr DynamicStereo: consistent dynamic depth from stereo videos
title_full_unstemmed DynamicStereo: consistent dynamic depth from stereo videos
title_short DynamicStereo: consistent dynamic depth from stereo videos
title_sort dynamicstereo consistent dynamic depth from stereo videos
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AT neverovan dynamicstereoconsistentdynamicdepthfromstereovideos
AT vedaldia dynamicstereoconsistentdynamicdepthfromstereovideos
AT rupprechtc dynamicstereoconsistentdynamicdepthfromstereovideos