mlsanityData to 14 Oct 2026

GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network

arXiv 2112.06782 ↗First posted 13 Dec 2021cs.CVcited by 12

Not on the map. Why?

Abstract (arXiv)

Depth estimation is a challenging task of 3D reconstruction to enhance the accuracy sensing of environment awareness. This work brings a new solution with a set of improvements, which increase the quantitative and qualitative understanding of depth maps compared to existing methods. Recently, a convolutional neural network (CNN) has demonstrated its extraordinary ability in estimating depth maps from monocular videos. However, traditional CNN does not support topological structure and they can work only on regular image regions with determined size and weights. On the other hand, graph convolutional networks (GCN) can handle the convolution on non-Euclidean data and it can be applied to irregular image regions within a topological structure. Therefore, in this work in order to preserve object geometric appearances and distributions, we aim at exploiting GCN for a self-supervised depth estimation model. Our model consists of two parallel auto-encoder networks: the first is an auto-encoder that will depend on ResNet-50 and extract the feature from the input image and on multi-scale GCN to estimate the depth map. In turn, the second network will be used to estimate the ego-motion vector (i.e., 3D pose) between two consecutive frames based on ResNet-18. Both the estimated 3D pose and depth map will be used for constructing a target image. A combination of loss functions related to photometric, projection, and smoothness is used to cope with bad depth prediction and preserve the discontinuities of the objects. In particular, our method provided comparable and promising results with a high prediction accuracy of 89% on the publicly KITTI and Make3D datasets along with a reduction of 40% in the number of trainable parameters compared to the state of the art solutions. The source code is publicly available at https://github.com/ArminMasoumian/GCNDepth.git

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 49 of its 81 references that are in the map. First posted on arXiv 13 Dec 2021.

Builds on

49 of 81 references.

References · 49

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  6. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  7. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,102
  8. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  9. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
  10. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
  11. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 782
  12. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 723
37 more
  1. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  2. 20193D Packing for Self-Supervised Monocular Depth Estimationcited by 439
  3. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 403
  4. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  5. 2018GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Posecited by 342
  6. 2018Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentationcited by 222
  7. 2018Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraintscited by 179
  8. 2019Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Camerascited by 170
  9. 2018Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstructioncited by 159
  10. 2018Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videoscited by 155
  11. 2017Learning Depth from Monocular Videos using Direct Methodscited by 154
  12. 2018DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistencycited by 148
  13. 2019Self-Driving Cars: A Surveycited by 148
  14. 2020Feature-metric Loss for Self-supervised Learning of Depth and Egomotioncited by 140
  15. 2021Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Mergingcited by 104
  16. 2018Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understandingcited by 101
  17. 2020A Survey on Deep Learning Techniques for Stereo-based Depth Estimationcited by 90
  18. 2016Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networkscited by 73
  19. 2020Monocular Depth Estimation Based On Deep Learning: An Overviewcited by 68
  20. 2019DepthTransfer: Depth Extraction from Video Using Non-parametric Samplingcited by 60
  21. 2019Dual Graph Convolutional Network for Semantic Segmentationcited by 59
  22. 2018LEGO: Learning Edge with Geometry all at Once by Watching Videoscited by 47
  23. 2018Unsupervised Adversarial Depth Estimation using Cycled Generative Networkscited by 45
  24. 2021Self-supervised Monocular Depth Estimation for All Day Images using Domain Separationcited by 44
  25. 2017Unsupervised Learning of Geometry with Edge-aware Depth-Normal Consistencycited by 40
  26. 2021SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular imagescited by 33
  27. 2019Unsupervised High-Resolution Depth Learning From Videos With Dual Networkscited by 26
  28. 2019Attention-based Context Aggregation Network for Monocular Depth Estimationcited by 18
  29. 2018SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perceptioncited by 16
  30. 2019MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classificationcited by 13
  31. 2018A Deeper Insight into the UnDEMoN: Unsupervised Deep Network for Depth and Ego-Motion Estimationcited by 11
  32. 2019Depth Extraction from Video Using Non-parametric Samplingcited by 11
  33. 2021Absolute distance prediction based on deep learning object detection and monocular depth estimation modelscited by 8
  34. 2019Learn Stereo, Infer Mono: Siamese Networks for Self-Supervised, Monocular, Depth Estimationcited by 6
  35. 2019SimVODIS: Simultaneous Visual Odometry, Object Detection, and Instance Segmentationcited by 6
  36. 2018Low Power Depth Estimation of Rigid Objects for Time-of-Flight Imagingcited by 4
  37. 2018Using Curvilinear Features in Focus for Registering a Single Image to a 3D Objectcited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 8; 2024: 2; 2025: 1.

12 papers.

  1. 2022MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformercited by 136
  2. 2023LDM3D: Latent Diffusion Model for 3Dcited by 53
  3. 2023Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset Refinementcited by 10
  4. 2023Absorption-Based, Passive Range Imaging from Hyperspectral Thermal Measurementscited by 8
  5. 2023OCTraN: 3D Occupancy Convolutional Transformer Network in Unstructured Traffic Scenarioscited by 2
  6. 2023SOccDPT: Semi-Supervised 3D Semantic Occupancy from Dense Prediction Transformers trained under memory constraintscited by 2
  7. 2023EWasteNet: A Two-Stream Data Efficient Image Transformer Approach for E-Waste Classificationcited by 2
  8. 2024MaGRITTe: Manipulative and Generative 3D Realization from Image, Topview and Textcited by 2
  9. 2023HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality Decodercited by 1
  10. 2023Calibrating Panoramic Depth Estimation for Practical Localization and Mappingcited by 1
  11. 2024Depth Estimation Algorithm Based on Transformer-Encoder and Feature Fusioncited by 0
  12. 2025Co-SemDepth: Fast Joint Semantic Segmentation and Depth Estimation on Aerial Imagescited by 0