Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image

• URL: http://arxiv.org/abs/2103.07570v1
• Date: Fri, 12 Mar 2021 23:19:32 GMT
• Title: Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image
• Authors: Binghan Li, Yindong Hua, Yifeng Liu, Mi Lu
• Abstract summary: We present an advanced Dilated Fully Convolutional Neural Network to address the deficiencies of traditional CNNs. Taking advantages of the exponential expansion of the receptive field in dilated convolutions, our model can minimize the loss of resolution. We show experimentally on NYU Depth V2 datasets that the depth prediction obtained from our model is considerably closer to ground truth than that from traditional CNNs techniques.
• Score: 1.0131895986034314
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Depth prediction plays a key role in understanding a 3D scene. Several techniques have been developed throughout the years, among which Convolutional Neural Network has recently achieved state-of-the-art performance on estimating depth from a single image. However, traditional CNNs suffer from the lower resolution and information loss caused by the pooling layers. And oversized parameters generated from fully connected layers often lead to a exploded memory usage problem. In this paper, we present an advanced Dilated Fully Convolutional Neural Network to address the deficiencies. Taking advantages of the exponential expansion of the receptive field in dilated convolutions, our model can minimize the loss of resolution. It also reduces the amount of parameters significantly by replacing the fully connected layers with the fully convolutional layers. We show experimentally on NYU Depth V2 datasets that the depth prediction obtained from our model is considerably closer to ground truth than that from traditional CNNs techniques.

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