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

arXiv: https://arxiv.org/abs/2107.07596 · HTML: https://mlsanity.com/paper/2107.07596/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 18 references that are in the map.
- First posted on arXiv 15 Jul 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture](https://mlsanity.com/paper/1411.4734/) (2014)
- [From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation](https://mlsanity.com/paper/1907.10326/) (2019)
- [Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image](https://mlsanity.com/paper/1709.07492/) (2017)
- [Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera](https://mlsanity.com/paper/1807.00275/) (2018)
- [ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation](https://mlsanity.com/paper/2012.05258/) (2020)
- [Sparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation](https://mlsanity.com/paper/1808.00769/) (2018)
- [A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection](https://mlsanity.com/paper/2005.07431/) (2020)
- [Bidirectional Attention Network for Monocular Depth Estimation](https://mlsanity.com/paper/2009.00743/) (2020)
- [Depth Estimation from Monocular Images and Sparse Radar Data](https://mlsanity.com/paper/2010.00058/) (2020)
- [Multi-Modal Depth Estimation Using Convolutional Neural Networks](https://mlsanity.com/paper/2012.09667/) (2020)

## Built on by (most cited first, 30 of 33)

- [BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation](https://mlsanity.com/paper/2204.00987/) (2022)
- [OMNI-DC: Highly Robust Depth Completion with Multiresolution Depth Integration](https://mlsanity.com/paper/2411.19278/) (2024)
- [RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions](https://mlsanity.com/paper/2410.02924/) (2024)
- [RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric Scale](https://mlsanity.com/paper/2401.04325/) (2024)
- [EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditions](https://mlsanity.com/paper/2302.03860/) (2023)
- [Sparse Beats Dense: Rethinking Supervision in Radar-Camera Depth Completion](https://mlsanity.com/paper/2312.00844/) (2023)
- [CaFNet: A Confidence-Driven Framework for Radar Camera Depth Estimation](https://mlsanity.com/paper/2407.00697/) (2024)
- [MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources](https://mlsanity.com/paper/2601.22054/) (2026)
- [TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion](https://mlsanity.com/paper/2504.11773/) (2025)
- [RCDPT: Radar-Camera fusion Dense Prediction Transformer](https://mlsanity.com/paper/2211.02432/) (2022)
- [Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision](https://mlsanity.com/paper/2109.08880/) (2021)
- [GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsampling](https://mlsanity.com/paper/2409.02720/) (2024)
- [Advancing Autonomous Driving: DepthSense with Radar and Spatial Attention](https://mlsanity.com/paper/2109.05265/) (2021)
- [RIDERS: Radar-Infrared Depth Estimation for Robust Sensing](https://mlsanity.com/paper/2402.02067/) (2024)
- [Enhanced Radar Perception via Multi-Task Learning: Towards Refined Data for Sensor Fusion Applications](https://mlsanity.com/paper/2404.06165/) (2024)
- [LiRCDepth: Lightweight Radar-Camera Depth Estimation via Knowledge Distillation and Uncertainty Guidance](https://mlsanity.com/paper/2412.16380/) (2024)
- [Radar-Guided Polynomial Fitting for Metric Depth Estimation](https://mlsanity.com/paper/2503.17182/) (2025)
- [SpINR: Neural Volumetric Reconstruction for FMCW Radars](https://mlsanity.com/paper/2503.23313/) (2025)
- [Structure-Aware Radar-Camera Depth Estimation](https://mlsanity.com/paper/2506.05008/) (2025)
- [On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression](https://mlsanity.com/paper/2202.12369/) (2022)
- [How Much Depth Information can Radar Contribute to a Depth Estimation Model?](https://mlsanity.com/paper/2202.13220/) (2022)
- [RaCalNet: Radar Calibration Network for Sparse-Supervised Metric Depth Estimation](https://mlsanity.com/paper/2506.15560/) (2025)
- [JustDepth: Real-Time Radar-Camera Depth Estimation with Single-Scan LiDAR Supervision](https://mlsanity.com/paper/2607.22172/) (2026)
- [LiDAR Meta Depth Completion](https://mlsanity.com/paper/2307.12761/) (2023)
- [Optical Lens Attack on Monocular Depth Estimation for Autonomous Driving](https://mlsanity.com/paper/2411.00192/) (2024)
- [SpINRv2: Implicit Neural Representation for Passband FMCW Radars](https://mlsanity.com/paper/2506.08163/) (2025)
- [TRIDE: A Text-assisted Radar-Image weather-aware fusion network for Depth Estimation](https://mlsanity.com/paper/2508.08038/) (2025)
- [Selection, Not Fusion: Radar-Modulated State Space Models for Radar-Camera Depth Estimation](https://mlsanity.com/paper/2605.11840/) (2026)
- [Instance-Guided Radar Depth Estimation for 3D Object Detection](https://mlsanity.com/paper/2601.19314/) (2026)
- [Geometry-Aware Representation Denoising for Robust Multi-view 3D Reconstruction](https://mlsanity.com/paper/2605.26230/) (2026)

## 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.