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Conference Paper
Fast and Light-Weight Unsupervised Depth Estimation for Mobile GPU Hardware
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- Authors
- Sangyun Oh, Jongeun Lee, Hye-Jin S. Kim
- Issue Date
- 2018-06
- Citation
- Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2018, pp.1-3
- Language
- English
- Type
- Conference Paper
- Abstract
- Recently, deep architectures for depth estimation have been proposed for many applications as depth is a very essential element of 3D geometry in computer vision. However, we still need to make the architectures efficient in terms of model size and computation complexity in order for them to be adopted in mobile environments. While model size has been reduced greatly current state-of-the-art compression techniques, the amount of computation has not been much. In this paper, we propose a lightweight architecture that also reduces computational complexity by adopting RR(Reduction and Reconstruction) block. The design principles are motivated by Tiny-Darknet which is one of image classification architecture. We basically refer to existing compression techniques, but limit maximum number of convolutional layer expansions that have been implemented for performance preservation. Through this, total amount of computation does not increase more than a certain amount. Our architecture shows significant architectural savings 30 ∼ 59× in the number of trainable parameters compared to existing architectures. Furthermore, we demonstrated our architecture on the mobile GPU hardware and affords from 3.88 ∼ 5.37× less energy consumption and 2.54 ∼ 3.11× faster runtime while allows only 1.66%p ∼ 2.08%p performance degradation compare to our baseline application.
- KSP Keywords
- 3D geometry, Compression techniques, Computational complexity, Computer Vision(CV), Current state, Deep architecture, Depth estimation, Essential element, GPU hardware, Image Classification, Light-weight