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GTC ON-DEMAND

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Abstract:
We present a deep neural network architecture for estimating 3D depth from stereo images. The network is modeled after computer vision stereo matching pipelines to simplify training process. Our loss function consists of a photometric loss term and Lidar based loss terms. This combination makes it possible to train our DNN in a supervised, semi-supervised and completely unsupervised way. Our DNN produces depth maps that have accuracy similar to Lidar based depth. We also compare our stereo DNN architecture to other stereo architectures as well as to a monocular depth DNN architecture. We demonstrate qualitative and quantitative test results.
We present a deep neural network architecture for estimating 3D depth from stereo images. The network is modeled after computer vision stereo matching pipelines to simplify training process. Our loss function consists of a photometric loss term and Lidar based loss terms. This combination makes it possible to train our DNN in a supervised, semi-supervised and completely unsupervised way. Our DNN produces depth maps that have accuracy similar to Lidar based depth. We also compare our stereo DNN architecture to other stereo architectures as well as to a monocular depth DNN architecture. We demonstrate qualitative and quantitative test results.  Back
 
Topics:
Computer Vision, Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8660
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