3rd 3D-DLAD @IV'2021 workshop : sites.google.c...
Abstract : Cameras are ubiquitous, and video data is widely available. In this talk I will cover our work at TRI on self-supervised learning and ways to leverage projective geometry to learn from videos without any human supervision. Starting from the now standard paradigm of self-supervising depth in videos, I will also cover extensions to multi-camera systems, non-standard camera models, visual odometry and keypoint learning. In a semi-supervised setting, we have developed networks that can leverage partial point clouds both at training and inference time, for increased accuracy and robustness. Additionally, we have shown that raw unlabeled data can be used to bootstrap and significantly improve monocular 3D object detection. Finally, I will present recent work that uses self-supervised monocular depth estimation as a proxy task to improve sim-to-real unsupervised domain adaptation for semantic segmentation.
Негізгі бет Self-supervised 3D vision : Rareș Ambruș
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