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BoRe-Depth: Self-supervised Monocular Depth Estimation with Boundary Refinement for Embedded Systems

arXiv 2511.04388 ↗First posted 6 Nov 2025cs.CV cs.ROcited by 0

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Abstract (arXiv)

Depth estimation is one of the key technologies for realizing 3D perception in unmanned systems. Monocular depth estimation has been widely researched because of its low-cost advantage, but the existing methods face the challenges of poor depth estimation performance and blurred object boundaries on embedded systems. In this paper, we propose a novel monocular depth estimation model, BoRe-Depth, which contains only 8.7M parameters. It can accurately estimate depth maps on embedded systems and significantly improves boundary quality. Firstly, we design an Enhanced Feature Adaptive Fusion Module (EFAF) which adaptively fuses depth features to enhance boundary detail representation. Secondly, we integrate semantic knowledge into the encoder to improve the object recognition and boundary perception capabilities. Finally, BoRe-Depth is deployed on NVIDIA Jetson Orin, and runs efficiently at 50.7 FPS. We demonstrate that the proposed model significantly outperforms previous lightweight models on multiple challenging datasets, and we provide detailed ablation studies for the proposed methods. The code is available at https://github.com/liangxiansheng093/BoRe-Depth.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 38 of its 53 references that are in the map. First posted on arXiv 6 Nov 2025.

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38 of 53 references.

References · 38

  1. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  2. 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,360
  3. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  4. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  5. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  6. 2024Depth Pro: Sharp Monocular Metric Depth in Less Than a Secondcited by 404
  7. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  8. 2023Repurposing Diffusion-Based Image Generators for Monocular Depth Estimationcited by 327
  9. 2019Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Videocited by 244
  10. 2018Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstructioncited by 159
  11. 2018Evaluation of CNN-based Single-Image Depth Estimation Methodscited by 138
  12. 2020Semantically-Guided Representation Learning for Self-Supervised Monocular Depthcited by 134
26 more
  1. 2021MPViT: Multi-Path Vision Transformer for Dense Predictioncited by 122
  2. 2023The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimationcited by 116
  3. 2020HR-Depth: High Resolution Self-Supervised Monocular Depth Estimationcited by 115
  4. 2022Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimationcited by 100
  5. 2019FastDepth: Fast Monocular Depth Estimation on Embedded Systemscited by 94
  6. 2020Unsupervised Monocular Depth Learning in Dynamic Scenescited by 84
  7. 2024DepthFM: Fast Monocular Depth Estimation with Flow Matchingcited by 76
  8. 2016Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networkscited by 73
  9. 2019SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimationcited by 59
  10. 2021Fine-grained Semantics-aware Representation Enhancement for Self-supervised Monocular Depth Estimationcited by 55
  11. 2022SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenescited by 52
  12. 2023RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptionscited by 49
  13. 2021Toward Practical Monocular Indoor Depth Estimationcited by 42
  14. 2022UDepth: Fast Monocular Depth Estimation for Visually-guided Underwater Robotscited by 27
  15. 2024SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillationcited by 21
  16. 2023Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenescited by 20
  17. 2023WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather Conditionscited by 15
  18. 2022Lightweight Monocular Depth Estimation through Guided Decodingcited by 14
  19. 2023GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenescited by 13
  20. 2019SDNet: Semantically Guided Depth Estimation Networkcited by 11
  21. 2023SwinDepth: Unsupervised Depth Estimation using Monocular Sequences via Swin Transformer and Densely Cascaded Networkcited by 10
  22. 2021Boundary-induced and scene-aggregated network for monocular depth predictioncited by 8
  23. 2018CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networkscited by 4
  24. 2024GAM-Depth: Self-Supervised Indoor Depth Estimation Leveraging a Gradient-Aware Mask and Semantic Constraintscited by 3
  25. 2020Fast Depth Estimation for View Synthesiscited by 2
  26. 2023Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networkscited by 2

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