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MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation
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DOI：10.1109/TITS.2022.3179365.png)
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Depth estimation from a single RGB image has attracted great interest in autonomous driving and robotics. State-of-the-art methods are usually designed on top of complex and extremely deep network architectures, which require more computational resources. Moreover, the inherent characteristic of the backbone used by the existing approaches results in severe spatial information loss in the produced feature maps, which impairs the accuracy of depth estimation on small sized images. In this study, we aimed to design a novel and efficient Convolutional Neural Network (CNN) to address these problems. Specifically, we stacked two shallow encoder-decoder style subnetworks successively in a unified network. Extensive experiments have been conducted on the NYU depth v2, KITTI, Make3D and Unreal data sets. Experimental results show that the proposed network achieves comparable accuracy to state-of-the-art methods that have extremely deep architectures but runs at a much faster speed on a single, less powerful GPU.
Keyword:
Estimation
Convolution
Convolutional neural networks
Task analysis
Decoding
Computer architecture
Semantics
Monocular depth estimation
depth prediction
convolutional neural networks
encoder-decoder
autonomous vehicles
期刊
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8.4
论文数:
9.7K
被引数:
6.3W
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