mlsanityData to 7 Oct 2026

High-Precision Self-Supervised Monocular Depth Estimation with Rich-Resource Prior

arXiv 2408.00361 ↗First posted 1 Aug 2024cs.CVcited by 2

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Abstract (arXiv)

In the area of self-supervised monocular depth estimation, models that utilize rich-resource inputs, such as high-resolution and multi-frame inputs, typically achieve better performance than models that use ordinary single image input. However, these rich-resource inputs may not always be available, limiting the applicability of these methods in general scenarios. In this paper, we propose Rich-resource Prior Depth estimator (RPrDepth), which only requires single input image during the inference phase but can still produce highly accurate depth estimations comparable to rich resource based methods. Specifically, we treat rich-resource data as prior information and extract features from it as reference features in an offline manner. When estimating the depth for a single-image image, we search for similar pixels from the rich-resource features and use them as prior information to estimate the depth. Experimental results demonstrate that our model outperform other single-image model and can achieve comparable or even better performance than models with rich-resource inputs, only using low-resolution single-image input.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 40 references that are in the map. First posted on arXiv 1 Aug 2024.

Builds on

32 of 40 references.

References · 32

  1. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
  2. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 778
  3. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  4. 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 437
  5. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 379
  6. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  7. 2018Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentationcited by 222
  8. 2019High-Resolution Representations for Labeling Pixels and Regionscited by 211
  9. 2016DeMoN: Depth and Motion Network for Learning Monocular Stereocited by 200
  10. 2021The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depthcited by 190
  11. 2020On the uncertainty of self-supervised monocular depth estimationcited by 182
  12. 2019Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Camerascited by 170
20 more
  1. 2018DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistencycited by 148
  2. 2022MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformercited by 136
  3. 2019Self-Supervised Monocular Depth Hintscited by 127
  4. 2020HR-Depth: High Resolution Self-Supervised Monocular Depth Estimationcited by 115
  5. 2022Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimationcited by 100
  6. 2020A Survey on Deep Learning Techniques for Stereo-based Depth Estimationcited by 90
  7. 2020Unsupervised Monocular Depth Learning in Dynamic Scenescited by 84
  8. 2019How do neural networks see depth in single images?cited by 82
  9. 2021Self-Supervised Monocular Depth Estimation with Internal Feature Fusioncited by 67
  10. 2018SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimationcited by 64
  11. 2022Multi-Frame Self-Supervised Depth with Transformerscited by 64
  12. 2022Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depthcited by 48
  13. 2018Learning monocular depth estimation with unsupervised trinocular assumptionscited by 36
  14. 2020UnRectDepthNet: Self-Supervised Monocular Depth Estimation using a Generic Framework for Handling Common Camera Distortion Modelscited by 33
  15. 2023RM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic Scenescited by 22
  16. 2022Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information Fusioncited by 20
  17. 2020Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cuescited by 19
  18. 2022Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimationcited by 17
  19. 2023Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Networkcited by 14
  20. 2021Absolute distance prediction based on deep learning object detection and monocular depth estimation modelscited by 8

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Jasmine: Harnessing Diffusion Prior for Self-supervised Depth Estimationcited by 20
  2. 2025Hybrid-grained Feature Aggregation with Coarse-to-fine Language Guidance for Self-supervised Monocular Depth Estimationcited by 6