A real-time unsupervised monocular depth estimation method for outdoor scenes

Zhekai Bian; Xia Wang; Qiwei Liu; Shuaijun Lv; Ranfeng Wei

doi:10.1117/12.3037658

22 October 2024 A real-time unsupervised monocular depth estimation method for outdoor scenes

Zhekai Bian, Xia Wang, Qiwei Liu, Shuaijun Lv, Ranfeng Wei

Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 132740M (2024) https://doi.org/10.1117/12.3037658
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China

Abstract

Mainstream monocular depth estimation methods generally excel in accuracy but fall short in runtime performance. The main challenge in improving computational efficiency is to reduce the computation complexity and memory usage. Addressing this issue, we innovate an unsupervised monocular depth estimation method that not only achieves great real-time performance but also maintain high accuracy. We present a lightweight depth estimation network that leverages inverted residuals. Besides, we build a training scheme with multiple effective loss functions. Experimental validation on KITTI dataset demonstrates that our method not only rivals mainstream models in terms of accuracy but also exhibits lower number of parameters and FLOPs.

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

Citation Download Citation

Zhekai Bian, Xia Wang, Qiwei Liu, Shuaijun Lv, and Ranfeng Wei "A real-time unsupervised monocular depth estimation method for outdoor scenes", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 132740M (22 October 2024); https://doi.org/10.1117/12.3037658

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