mlsanityData to 7 Oct 2026

Depth Estimation from Monocular Images and Sparse radar using Deep Ordinal Regression Network

arXiv 2107.07596 ↗First posted 15 Jul 2021eess.IV cs.AI cs.CVcited by 32

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Abstract (arXiv)

We integrate sparse radar data into a monocular depth estimation model and introduce a novel preprocessing method for reducing the sparseness and limited field of view provided by radar. We explore the intrinsic error of different radar modalities and show our proposed method results in more data points with reduced error. We further propose a novel method for estimating dense depth maps from monocular 2D images and sparse radar measurements using deep learning based on the deep ordinal regression network by Fu et al. Radar data are integrated by first converting the sparse 2D points to a height-extended 3D measurement and then including it into the network using a late fusion approach. Experiments are conducted on the nuScenes dataset. Our experiments demonstrate state-of-the-art performance in both day and night scenes.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 16 of its 18 references that are in the map. First posted on arXiv 15 Jul 2021.

Builds on

16 of 18 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  4. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  5. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  6. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
  7. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  8. 2019From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimationcited by 379
  9. 2017Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Imagecited by 159
  10. 2018Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Cameracited by 140
  11. 2020ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentationcited by 95
  12. 2018Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentationcited by 76
4 more
  1. 2020A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detectioncited by 70
  2. 2020Bidirectional Attention Network for Monocular Depth Estimationcited by 48
  3. 2020Depth Estimation from Monocular Images and Sparse Radar Datacited by 44
  4. 2020Multi-Modal Depth Estimation Using Convolutional Neural Networkscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 3; 2024: 9; 2025: 7; 2026: 7.

32 papers.

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  1. 2022BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimationcited by 166
  2. 2024OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integrationcited by 28
  3. 2024RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric Scalecited by 20
  4. 2024RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptionscited by 20
  5. 2023EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditionscited by 19
  6. 2023Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completioncited by 12
  7. 2024CaFNet: A Confidence-Driven Framework for Radar Camera Depth Estimationcited by 12
  8. 2026MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sourcescited by 10
  9. 2025TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusioncited by 9
  10. 2021Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Visioncited by 7
  11. 2022RCDPT: Radar-Camera fusion Dense Prediction Transformercited by 7
  12. 2024GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsamplingcited by 7
20 more
  1. 2021Advancing Autonomous Driving: DepthSense with Radar and Spatial Attentioncited by 5
  2. 2024RIDERS: Radar-Infrared Depth Estimation for Robust Sensingcited by 5
  3. 2024Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applicationscited by 5
  4. 2024LiRCDepth: Lightweight Radar-Camera Depth Estimation via Knowledge Distillation and Uncertainty Guidancecited by 5
  5. 2025Radar-Guided Polynomial Fitting for Metric Depth Estimationcited by 5
  6. 2025SpINR: Neural Volumetric Reconstruction for FMCW Radarscited by 5
  7. 2025Structure-Aware Radar-Camera Depth Estimationcited by 5
  8. 2022On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regressioncited by 2
  9. 2022How Much Depth Information can Radar Contribute to a Depth Estimation Model?cited by 2
  10. 2025RaCalNet: Radar Calibration Network for Sparse-Supervised Metric Depth Estimationcited by 2
  11. 2026JustDepth: Real-Time Radar-Camera Depth Estimation with Single-Scan LiDAR Supervisioncited by 2
  12. 2023LiDAR Meta Depth Completioncited by 1
  13. 2025SpINRv2: Implicit Neural Representation for Passband FMCW Radarscited by 1
  14. 2025TRIDE: A Text-assisted Radar-Image weather-aware fusion network for Depth Estimationcited by 1
  15. 2026Selection, Not Fusion: Radar-Modulated State Space Models for Radar-Camera Depth Estimationcited by 1
  16. 2024Optical Lens Attack on Monocular Depth Estimation for Autonomous Drivingcited by 0
  17. 2026Instance-Guided Radar Depth Estimation for 3D Object Detectioncited by 0
  18. 2026Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstructioncited by 0
  19. 2026Boosting Robustness for All-Weather Self-Supervised Depth Estimation in Autonomous Drivingcited by 0
  20. 2026RbFT-Net: Rectify-Before-Fuse Temporal Radar Anchors for 4D Radar-Camera Depth Completioncited by 0