mlsanityData to 7 Oct 2026

EdgeConv with Attention Module for Monocular Depth Estimation

arXiv 2106.08615 ↗First posted 16 Jun 2021cs.CVcited by 9

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Abstract (arXiv)

Monocular depth estimation is an especially important task in robotics and autonomous driving, where 3D structural information is essential. However, extreme lighting conditions and complex surface objects make it difficult to predict depth in a single image. Therefore, to generate accurate depth maps, it is important for the model to learn structural information about the scene. We propose a novel Patch-Wise EdgeConv Module (PEM) and EdgeConv Attention Module (EAM) to solve the difficulty of monocular depth estimation. The proposed modules extract structural information by learning the relationship between image patches close to each other in space using edge convolution. Our method is evaluated on two popular datasets, the NYU Depth V2 and the KITTI Eigen split, achieving state-of-the-art performance. We prove that the proposed model predicts depth robustly in challenging scenes through various comparative experiments.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 37 of its 50 references that are in the map. First posted on arXiv 16 Jun 2021.

Builds on

37 of 50 references.

References · 37

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  5. 2016Densely Connected Convolutional Networkscited by 6,691
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  7. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  8. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  9. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  10. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  11. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  12. 2017Non-local Neural Networkscited by 2,290
25 more
  1. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  2. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  3. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  4. 2016Geometric deep learning: going beyond Euclidean datacited by 1,042
  5. 2018Dual Attention Network for Scene Segmentationcited by 1,001
  6. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
  7. 2018Self-Attention Generative Adversarial Networkscited by 809
  8. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
  9. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  10. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 379
  11. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  12. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
  13. 2019Enforcing geometric constraints of virtual normal for depth predictioncited by 233
  14. 2018PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsingcited by 224
  15. 2018BAM: Bottleneck Attention Modulecited by 171
  16. 2018Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundariescited by 144
  17. 2019Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentationcited by 141
  18. 2017Semi-Supervised Deep Learning for Monocular Depth Map Predictioncited by 109
  19. 2018Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimationcited by 78
  20. 2017Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimationcited by 70
  21. 2019DepthTransfer: Depth Extraction from Video Using Non-parametric Samplingcited by 60
  22. 2019Structure-Aware Residual Pyramid Network for Monocular Depth Estimationcited by 49
  23. 2019Attention-based Context Aggregation Network for Monocular Depth Estimationcited by 18
  24. 2016Depth from a Single Image by Harmonizing Overcomplete Local Network Predictionscited by 16
  25. 2020Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasetscited by 14

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 3; 2025: 1.

9 papers.

  1. 2022Attention Attention Everywhere: Monocular Depth Prediction with Skip Attentioncited by 92
  2. 2021ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimationcited by 39
  3. 2024Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoningcited by 12
  4. 2022Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Drivingcited by 8
  5. 2023An intelligent modular real-time vision-based system for environment perceptioncited by 1
  6. 2023HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality Decodercited by 1
  7. 2024STATIC : Surface Temporal Affine for TIme Consistency in Video Monocular Depth Estimationcited by 1
  8. 2025EuLearn: A 3D database for learning Euler characteristicscited by 1
  9. 2024PMPNet: Pixel Movement Prediction Network for Monocular Depth Estimation in Dynamic Scenescited by 0