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GTC ON-DEMAND

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Abstract:
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 in 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.
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 in 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.  Back
 
Topics:
Computer Vision, Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9317
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Abstract:
Learn how to predict a dense depth image from a sparse set of depth measurements and a single RGB image. This approach can be applied to serve as a plug-in module in simultaneous localization and mapping to convert sparse maps to dense maps, and as a super-resolution of LiDAR depth data. We''ll describe the performance of our prediction method, explain how to train the depth prediction network, and showcase examples of its applications. Codes and video demonstration are also publicly available. This session is for registrants who are already familiar with basic machine learning techniques.
Learn how to predict a dense depth image from a sparse set of depth measurements and a single RGB image. This approach can be applied to serve as a plug-in module in simultaneous localization and mapping to convert sparse maps to dense maps, and as a super-resolution of LiDAR depth data. We''ll describe the performance of our prediction method, explain how to train the depth prediction network, and showcase examples of its applications. Codes and video demonstration are also publicly available. This session is for registrants who are already familiar with basic machine learning techniques.  Back
 
Topics:
AI & Deep Learning Research, Computer Vision
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8216
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