تقرير
Multi-view Monocular Depth and Uncertainty Prediction with Deep SfM in Dynamic Environments
| العنوان: | Multi-view Monocular Depth and Uncertainty Prediction with Deep SfM in Dynamic Environments |
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| المؤلفون: | Homeyer, Christian, Lange, Oliver, Schnörr, Christoph |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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 \cite{teed2018deepv2d} with a learned uncertainty. Our 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 a simple filter strategy based on the uncertainty can significantly reduce systematic errors. This results in cleaner reconstructions both on static and dynamic parts of the scene. Comment: 20 pages, 5 figures, 3 tables, submitted to ICPRAI 2022 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2201.08633 |
| رقم الأكسشن: | edsarx.2201.08633 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |