FasterDepth: a lightweight network for self-supervised monocular depth estimation

Wei Li

doi:10.1117/12.3039064

13 September 2024 FasterDepth: a lightweight network for self-supervised monocular depth estimation

Wei Li

Proceedings Volume 13254, Fourth International Conference on Optics and Image Processing (ICOIP 2024); 132540Y (2024) https://doi.org/10.1117/12.3039064
Event: Fourth International Conference on Optics and Image Processing (ICOIP 2024), 2024, Chongqing, China

Abstract

Self-supervised monocular depth estimation uses only one camera to get depth information. It does not need manually real depth information as training data, but is trained by the geometric information contained in the image itself. While many existing methods use heavy backbone networks for precision, designing lightweight models can reduce the computational and memory consumption, making them suitable for resource-constrained environments or embedded devices. In this work, we propose a lightweight network (FasterDepth) for self-supervised monocular depth estimation. Additionally, in order to merge the rich information of multi-stage of the network, this work raises a multi-stage feature fusion module. Experiments on the KITTI dataset show that our FasterDepth has higher precision and fewer parameters than Monodepth2.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wei Li "FasterDepth: a lightweight network for self-supervised monocular depth estimation", Proc. SPIE 13254, Fourth International Conference on Optics and Image Processing (ICOIP 2024), 132540Y (13 September 2024); https://doi.org/10.1117/12.3039064

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KEYWORDS

Education and training

Feature fusion

Image restoration

Visualization

Ablation

Convolution

Network architectures

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