# GEDepth: Ground Embedding for Monocular Depth Estimation

arXiv: https://arxiv.org/abs/2309.09975 · HTML: https://mlsanity.com/paper/2309.09975/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 35 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 30 of 32)

- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection](https://mlsanity.com/paper/2206.10092/) (2022)
- [Pseudo-LiDAR from Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving](https://mlsanity.com/paper/1812.07179/) (2018)
- [3D Packing for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/1905.02693/) (2019)
- [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://mlsanity.com/paper/1606.00373/) (2016)
- [From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation](https://mlsanity.com/paper/1907.10326/) (2019)
- [Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue](https://mlsanity.com/paper/1603.04992/) (2016)
- [Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints](https://mlsanity.com/paper/1802.05522/) (2018)
- [BinsFormer: Revisiting Adaptive Bins for Monocular Depth Estimation](https://mlsanity.com/paper/2204.00987/) (2022)
- [CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction](https://mlsanity.com/paper/1704.03489/) (2017)
- [DepthFormer: Exploiting Long-Range Correlation and Local Information for Accurate Monocular Depth Estimation](https://mlsanity.com/paper/2203.14211/) (2022)
- [Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention](https://mlsanity.com/paper/2210.09071/) (2022)
- [How do neural networks see depth in single images?](https://mlsanity.com/paper/1905.07005/) (2019)
- [Exploring Simple 3D Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2108.10312/) (2021)
- [PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds](https://mlsanity.com/paper/2305.04925/) (2023)
- [Normal Assisted Stereo Depth Estimation](https://mlsanity.com/paper/1911.10444/) (2019)
- [ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals](https://mlsanity.com/paper/2303.12071/) (2023)
- [Self-Supervised Pillar Motion Learning for Autonomous Driving](https://mlsanity.com/paper/2104.08683/) (2021)
- [Adaptive Surface Normal Constraint for Depth Estimation](https://mlsanity.com/paper/2103.15483/) (2021)
- [Sparse Auxiliary Networks for Unified Monocular Depth Prediction and Completion](https://mlsanity.com/paper/2103.16690/) (2021)
- [Bi3D: Stereo Depth Estimation via Binary Classifications](https://mlsanity.com/paper/2005.07274/) (2020)
- [Visualization of Convolutional Neural Networks for Monocular Depth Estimation](https://mlsanity.com/paper/1904.03380/) (2019)
- [From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction](https://mlsanity.com/paper/2203.08122/) (2022)
- [Transcendental Idealism of Planner: Evaluating Perception from Planning Perspective for Autonomous Driving](https://mlsanity.com/paper/2306.07276/) (2023)
- [PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation](https://mlsanity.com/paper/2006.02068/) (2020)

## Built on by (most cited first, 26 of 26)

- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation](https://mlsanity.com/paper/2403.18807/) (2024)
- [Vision-Language Embodiment for Monocular Depth Estimation](https://mlsanity.com/paper/2503.16535/) (2025)
- [EVP: Enhanced Visual Perception using Inverse Multi-Attentive Feature Refinement and Regularized Image-Text Alignment](https://mlsanity.com/paper/2312.08548/) (2023)
- [Harnessing Diffusion Models for Visual Perception with Meta Prompts](https://mlsanity.com/paper/2312.14733/) (2023)
- [FutureDepth: Learning to Predict the Future Improves Video Depth Estimation](https://mlsanity.com/paper/2403.12953/) (2024)
- [SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2404.12501/) (2024)
- [UVCPNet: A UAV-Vehicle Collaborative Perception Network for 3D Object Detection](https://mlsanity.com/paper/2406.04647/) (2024)
- [GroCo: Ground Constraint for Metric Self-Supervised Monocular Depth](https://mlsanity.com/paper/2409.14850/) (2024)
- [CHARM3R: Towards Unseen Camera Height Robust Monocular 3D Detector](https://mlsanity.com/paper/2508.11185/) (2025)
- [Structure-Aware Radar-Camera Depth Estimation](https://mlsanity.com/paper/2506.05008/) (2025)
- [GVDepth: Zero-Shot Monocular Depth Estimation for Ground Vehicles based on Probabilistic Cue Fusion](https://mlsanity.com/paper/2412.06080/) (2024)
- [Scale Equalization for Multi-Level Feature Fusion](https://mlsanity.com/paper/2402.01149/) (2024)
- [STATIC : Surface Temporal Affine for TIme Consistency in Video Monocular Depth Estimation](https://mlsanity.com/paper/2412.01090/) (2024)
- [TR2M: Transferring Monocular Relative Depth to Metric Depth with Language Descriptions and Dual-Level Scale-Oriented Contrast](https://mlsanity.com/paper/2506.13387/) (2025)
- [Shoe Style-Invariant and Ground-Aware Learning for Dense Foot Contact Estimation](https://mlsanity.com/paper/2511.22184/) (2025)
- [FA-Depth: Toward Fast and Accurate Self-supervised Monocular Depth Estimation](https://mlsanity.com/paper/2405.10885/) (2024)
- [Is Pre-training Applicable to the Decoder for Dense Prediction?](https://mlsanity.com/paper/2503.07637/) (2025)
- [Expanding-and-Shrinking Binary Neural Networks](https://mlsanity.com/paper/2503.23709/) (2025)
- [BRIDGE -- Building Reinforcement-Learning Depth-to-Image Data Generation Engine for Monocular Depth Estimation](https://mlsanity.com/paper/2509.25077/) (2025)
- [ReDepth Anything: Test-Time Depth Refinement via Self-Supervised Re-lighting](https://mlsanity.com/paper/2512.17908/) (2025)
- [Monocular Depth Estimation From the Perspective of Feature Restoration: A Diffusion Enhanced Depth Restoration Approach](https://mlsanity.com/paper/2604.07664/) (2026)
- [Last-Layer-Centric Feature Recombination: Unleashing 3D Geometric Knowledge in DINOv3 for Monocular Depth Estimation](https://mlsanity.com/paper/2604.26454/) (2026)
- [Repurposing RGB-based Foundation Model for Depth Estimation on Thermal Images Using Hierarchical Supervision](https://mlsanity.com/paper/2608.11564/) (2026)
- [Monocular Depth Estimation from a Single Image: Progress and Opportunities](https://mlsanity.com/paper/2609.01172/) (2026)

## Abstract (arXiv)

Monocular depth estimation is an ill-posed problem as the same 2D image can be projected from infinite 3D scenes. Although the leading algorithms in this field have reported significant improvement, they are essentially geared to the particular compound of pictorial observations and camera parameters (i.e., intrinsics and extrinsics), strongly limiting their generalizability in real-world scenarios. To cope with this challenge, this paper proposes a novel ground embedding module to decouple camera parameters from pictorial cues, thus promoting the generalization capability. Given camera parameters, the proposed module generates the ground depth, which is stacked with the input image and referenced in the final depth prediction. A ground attention is designed in the module to optimally combine ground depth with residual depth. Our ground embedding is highly flexible and lightweight, leading to a plug-in module that is amenable to be integrated into various depth estimation networks. Experiments reveal that our approach achieves the state-of-the-art results on popular benchmarks, and more importantly, renders significant generalization improvement on a wide range of cross-domain tests.