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2019 GTC San Jose

S9317 - Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera

Session Speakers
Session Description

We'll present our research work on self-supervised depth completion — the technique of predicting a dense depth image from only sparse depth measurements (e.g., from LiDAR), which has applications robotics and autonomous driving. To address the problem of depth completion, we develop a deep regression model to learn the mapping. Our model was the winning approach on the KITTI depth completion competition in 2018. Beyond that work, we propose a self-supervised training framework for training the depth completion neural network that that would require only a sequence of color and sparse depth images, without the need for any dense ground truth depth labels, which are difficult to obtain. Our experiments demonstrate that the self-supervised framework outperforms a number of existing solutions trained with semi-dense ground truth annotations.


Additional Information
Computer Vision
Computer Vision, Autonomous Vehicles
Software, Higher Education / Research
All technical
Talk
50 minutes
Session Schedule