Research on monocular depth estimation algorithm based on improved DNA-Depth

Ling Wang; Yaokang Li; Peng Wang; Yane Bai

doi:10.1117/12.3048066

3 October 2024 Research on monocular depth estimation algorithm based on improved DNA-Depth

Ling Wang, Yaokang Li, Peng Wang, Yane Bai

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Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132720J (2024) https://doi.org/10.1117/12.3048066
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

DNA-Depth is currently one of the better-performing lightweight models for monocular depth estimation, but predicting detailed information remains challenging. This paper proposes a method called EUF-Depth, which can efficiently fuse encoder features based on DNA-Depth. Firstly, a feature fusion strategy is introduced to improve the utilization of important features through a learnable method. Then, the channel attention in the decoder part is replaced with Coordinate Attention (CA), incorporating positional information to enhance model prediction accuracy. Experiments on the KITTI benchmark demonstrate that EUF-Depth outperforms DNA-Depth in all metrics. The error evaluation metrics decrease by an average of 2% and the accuracy evaluation metrics increase by an average of 1%.

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

Citation Download Citation

Ling Wang, Yaokang Li, Peng Wang, and Yane Bai "Research on monocular depth estimation algorithm based on improved DNA-Depth", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132720J (3 October 2024); https://doi.org/10.1117/12.3048066

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KEYWORDS

Design for manufacturing

Feature fusion

Convolution

Depth maps

Education and training

Image fusion

Feature extraction

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