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September 27, 2025Applied SciencesOpen Access

MonoLENS: Monocular Lightweight Efficient Network with Separable Convolutions for Self-Supervised Monocular Depth Estimation

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MonoLENS reduces model weight and improves accuracy in self-supervised monocular depth estimation tasks, highlighting efficient design.

MonoLENS reduces parameters by 42% compared to Lite-Mono while enhancing geometric fidelity.
Monocular depth estimation achieved leading performance on the KITTI benchmark, improving accuracy by roughly 4.5%.
The model incorporates depthwise separable convolutions and Multidimensional Collaborative Attention to optimize computation.
Significant architecture simplifications allowed maintaining performance while improving efficiency across edge devices.

Higashiuchi et al. (2025) studied this question.

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