Data to 7 Oct 2026

Guiding Monocular Depth Estimation Using Depth-Attention Volume

arXiv 2004.02760 ↗First posted 6 Apr 2020cs.CVcited by 82

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Abstract (arXiv)

Recovering the scene depth from a single image is an ill-posed problem that requires additional priors, often referred to as monocular depth cues, to disambiguate different 3D interpretations. In recent works, those priors have been learned in an end-to-end manner from large datasets by using deep neural networks. In this paper, we propose guiding depth estimation to favor planar structures that are ubiquitous especially in indoor environments. This is achieved by incorporating a non-local coplanarity constraint to the network with a novel attention mechanism called depth-attention volume (DAV). Experiments on two popular indoor datasets, namely NYU-Depth-v2 and ScanNet, show that our method achieves state-of-the-art depth estimation results while using only a fraction of the number of parameters needed by the competing methods.

Key facts. Cited by 82 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2020.

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Citing papers per year: 2021: 19; 2022: 27; 2023: 19; 2024: 10; 2025: 4; 2026: 3.

82 papers.

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2022BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimationcited by 166
2022NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimationcited by 151
2023DDP: Diffusion Model for Dense Visual Predictioncited by 148
2021Transformer-Based Attention Networks for Continuous Pixel-Wise Predictioncited by 125
2022DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimationcited by 116
2022P3Depth: Monocular Depth Estimation with a Piecewise Planarity Priorcited by 106
2021Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Mergingcited by 104
2022LocalBins: Improving Depth Estimation by Learning Local Distributionscited by 92
2022Attention Attention Everywhere: Monocular Depth Prediction with Skip Attentioncited by 92
2023Monocular Depth Estimation using Diffusion Modelscited by 87
2023DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimationcited by 81
2023iDisc: Internal Discretization for Monocular Depth Estimationcited by 71

38 more

2021Unsupervised Scale-consistent Depth Learning from Videocited by 69
2022Multi-Frame Self-Supervised Depth with Transformerscited by 64
2022Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepthcited by 58
2023IEBins: Iterative Elastic Bins for Monocular Depth Estimationcited by 53
2021Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Predictioncited by 48
2023NDDepth: Normal-Distance Assisted Monocular Depth Estimationcited by 45
2021Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objectscited by 39
2021Input-level Inductive Biases for 3D Reconstructioncited by 31
2022IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertaintycited by 30
2024Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Drivingcited by 30
2021Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimationcited by 27
2022DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Imagecited by 26
2023DiffI2I: Efficient Diffusion Model for Image-to-Image Translationcited by 24
2023SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimationcited by 21
2022Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information Fusioncited by 20
2022Is my Depth Ground-Truth Good Enough? HAMMER -- Highly Accurate Multi-Modal Dataset for DEnse 3D Scene Regressioncited by 18
2021Boosting Light-Weight Depth Estimation Via Knowledge Distillationcited by 16
2022Depth Map Decomposition for Monocular Depth Estimationcited by 16
2023URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimationcited by 16
2025Vision-Language Embodiment for Monocular Depth Estimationcited by 16
2024Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Surveycited by 15
2024EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy Labelscited by 12
2022High-Resolution Depth Estimation for 360-degree Panoramas through Perspective and Panoramic Depth Images Registrationcited by 10
2023High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimationcited by 10
2022GeoFill: Reference-Based Image Inpainting with Better Geometric Understandingcited by 9
2022PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimationcited by 9
2021PlaneRecNet: Multi-Task Learning with Cross-Task Consistency for Piece-Wise Plane Detection and Reconstruction from a Single RGB Imagecited by 8
2022Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth Estimationcited by 8
2023NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completioncited by 8
2024SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimationcited by 8
2021Towards Comprehensive Monocular Depth Estimation: Multiple Heads Are Better Than Onecited by 7
2021NVS-MonoDepth: Improving Monocular Depth Prediction with Novel View Synthesiscited by 7
2022Egocentric Scene Understanding via Multimodal Spatial Rectifiercited by 7
2021Lightweight Monocular Depth with a Novel Neural Architecture Search Methodcited by 6
20223D Scene Inference from Transient Histogramscited by 6
2023S2Net: Accurate Panorama Depth Estimation on Spherical Surfacecited by 6
2021Invertible Attentioncited by 5
2024SDGE: Stereo Guided Depth Estimation for 360^circ Camera Setscited by 5

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