What does this research mean for the field?

A novel lightweight self-supervised monocular depth estimation model incorporating Asymmetric Dilated Convolution and StarNext modules reduces parameters and computational load by over 50% and achieves significant inference speedups on edge devices without compromising accuracy compared to baseline models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To design a lightweight self-supervised monocular depth estimation network optimized for real-time inference on resource-constrained edge devices.

August 23, 2026SensorsOpen Access

Lightweight Monocular Depth Estimation with Local Feature Enhancement Modules and Guided Data Augmentation

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Authors

JLJae-Young LeeUlsan College SKSoon-kak KwonDong-Eui University

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Implication

Benchmarking study demonstrates efficient self-supervised monocular depth estimation on edge hardware, indicating feasibility for real-time robotic vision systems.

Key Points

To design a lightweight self-supervised monocular depth estimation network optimized for real-time inference on resource-constrained edge devices.
Halved feature-extracting iterations and introduced Asymmetric Dilated Convolution modules for directional features alongside StarNext modules for multi-scale feature fusion.
Applied random cropping, scaling, and a Disparity-guided Cutout technique that masks adjacent pixels based on pre-inferred disparity maps during training.
Evaluated performance against baseline Lite-Mono on the KITTI dataset and measured real-time inference across four power modes (MaxN, 25 W, 15 W, and 7 W) on an NVIDIA Jetson Orin Nano.
Reduced model parameters by approximately 50% and GFLOPs by approximately 58% on KITTI without significant degradation in depth estimation accuracy compared to Lite-Mono.
Achieved inference runtime speedups on the Jetson Orin Nano of 46.0% (MaxN), 46.1% (25 W), 46.3% (15 W), and 52.0% (7 W).

Cite This Study

Lee et al. (2026) studied this question.

synapsesocial.com/papers/6a8aadf37677a3411444677b https://doi.org/10.3390/s26165306

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Implication

Benchmarking study demonstrates efficient self-supervised monocular depth estimation on edge hardware, indicating feasibility for real-time robotic vision systems.

Lightweight Monocular Depth Estimation with Local Feature Enhancement Modules and Guided Data Augmentation

Discussion

Member takes

Implication

Key Points

Cite This Study

Lightweight Monocular Depth Estimation with Local Feature Enhancement Modules and Guided Data Augmentation

Discussion

Member takes

Implication

Key Points

Cite This Study

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