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- W4200355967 abstract "Abstract With the development of depth learning, the accuracy and effect of the algorithm applied to monocular depth estimation have been greatly improved, but the existing algorithms need a lot of computing resources. At present, how to apply the existing algorithms to UAV and its small robot is an urgent need.Based on full convolution neural network and Kitti dataset, this paper uses deep separable convolution to optimize the network architecture, reduce training parameters and improve computing speed. Experimental results show that our method is very effective and has a certain reference value in the development direction of monocular depth estimation algorithm." @default.
- W4200355967 created "2021-12-31" @default.
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- W4200355967 date "2021-12-17" @default.
- W4200355967 modified "2023-10-16" @default.
- W4200355967 title "OptiDepthNet : A Real-time Unsupervised Monocular Depth Estimation Network" @default.
- W4200355967 doi "https://doi.org/10.21203/rs.3.rs-812743/v1" @default.
- W4200355967 hasPublicationYear "2021" @default.
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