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- W4285259519 abstract "3D reconstruction of depth and motion from monocular video in dynamic environments is a highly ill-posed problem due to scale ambiguities when projecting to the 2D image domain. In this work, we investigate the performance of the current State-of-the-Art (SotA) deep multi-view systems in such environments. We find that current supervised methods work surprisingly well despite not modelling individual object motions, but make systematic errors due to a lack of dense ground truth data. To detect such errors during usage, we extend the cost volume based Deep Video to Depth (DeepV2D) framework [26] with a learned uncertainty. Our resulting Deep Video to certain Depth (DeepV2cD) model allows (i) to perform en par or better with current SotA and (ii) achieve a better uncertainty measure than the naive Shannon entropy. Our experiments show that we can significantly reduce systematic errors by taking the uncertainty into account. This results in more accurate reconstructions both on static and dynamic parts of the scene." @default.
- W4285259519 created "2022-07-14" @default.
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- W4285259519 date "2022-01-01" @default.
- W4285259519 modified "2023-09-27" @default.
- W4285259519 title "Multi-view Monocular Depth and Uncertainty Prediction with Deep SfM in Dynamic Environments" @default.
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- W4285259519 doi "https://doi.org/10.1007/978-3-031-09037-0_31" @default.
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