Cocktail Glass Network: Fast Depth Estimation Using Channel to Space Unrolling

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Depth-estimation from a single input image can be used in applications such as robotics and autonomous driving. Recently, depth-estimation networks with UNet encoder/decoder structures have been widely used. In these decoders, operations are repeated to gradually increase the image resolution, while decreasing the channel size. If the upsampling operation at a high magnification can be processed at once, the amount of computation in the decoder can be dramatically reduced. To achieve this, we propose a new network structure, i.e., a cocktail glass network. In this network, convolution layers in the decoder are reduced, and a novel fast upsampling method is used that is known as channel-to-space unrolling, which converts thick channel data into high-resolution data. The proposed method can be easily implemented using simple reshaping operations; therefore, it is suitable for reducing the depth-estimation network. Considering the experimental results based on the NYU V2 and KITTI datasets, we demonstrate that the proposed method reduces the amount of computation in the decoder by half, while maintaining the same level of accuracy; it can be used in both lightweight and large-model-capacity networks.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021
Language
English
Article Type
Article
Citation

IEEE ACCESS, v.9, pp.114680 - 114689

ISSN
2169-3536
DOI
10.1109/ACCESS.2021.3105136
URI
http://hdl.handle.net/10203/287679
Appears in Collection
EE-Journal Papers(저널논문)
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