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Conference Paper
Monocular Depth Estimation for Mobile Device
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- Authors
- Yongsik Lee, Seungjae Lee, Jong Gook Ko
- Issue Date
- 2021-11
- Citation
- International Conference on Consumer Electronics (ICCE) 2021 : Asia, pp.498-500
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Depth estimation is an important work in inferring scene geometry from 2D images applied in robotics, AR, autonomous driving, etc. Especially, monocular depth estimation is required for wide application in mobile devices. In this paper, a lightweight monocular depth estimation method is proposed for mobile devices and applied to mobile devices by changing the loss function and using network slimming. The result shows that our model requires low memory and reduces computational complexity that is suitable for mobile devices. The mobile device that we deploy is iPhone 11 Pro and it operates at over 30fps.
- KSP Keywords
- Computational complexity, Mobile devices, Monocular depth estimation, autonomous driving, estimation method, loss function