Real-time lightweight self-supervised monocular depth estimation.
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| Title: | Real-time lightweight self-supervised monocular depth estimation. |
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| Authors: | YANG, Tianxiang1, MENG, Lingjun1 menglingjun@nuc.edu.cn, JIN, Hong2, FENG, Wenjie1, LIU, Xinhao3 |
| Source: | Journal of Measurement Science & Instrumentation. Jun2026, Vol. 17 Issue 2, p278-296. 19p. |
| Subjects: | Depth maps (Digital image processing), Real-time computing, Artificial neural networks, Machine learning, Computer vision, Edge computing |
| Abstract: | Monocular depth estimation aims to predict depth information within a scene from a single RGB image, but many models remain computationally intensive for real-time inference on resource-constrained edge devices. This paper presents a lightweight self-supervised monocular depth estimation network that balances accuracy and efficiency through targeted encoder - decoder design. The encoder employed a synergistic modeling approach combining decomposable large-kernel convolutions and local depthwise convolutions to capture both long-range context and local details with low computational overhead. The decoder utilized cross-scale feature differences as guidance to dynamically fuse multi-scale features, enhancing detail recovery and geometric consistency under lightweight constraints. In addition, a temporal soft fusion reprojection loss was employed to better leverage the complementary information of forward and backward frames, improving the robustness of self-supervised training. The model contained 3.0 M parameters and required 3.5 GFLOPs of computation. On KITTI, it achieves Abs Rel=0.105 and δ1=0.892. On Make3D, it achieves Abs Rel=0.308 in a zero-shot setting. On a Rockchip RK3588S, a hybrid-quantized multi-thread implementation runs at 67 frames/s. The results demonstrated that the proposed method achieved a favorable accuracy-efficiency balance on edge devices, making it suitable for real-time monocular depth estimation tasks. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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