# Introduction to Camera Pose Estimation with Deep Learning

arXiv: https://arxiv.org/abs/1907.05272 · HTML: https://mlsanity.com/paper/1907.05272/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 25 of 25)

- [LENS: Localization enhanced by NeRF synthesis](https://mlsanity.com/paper/2110.06558/) (2021)
- [Learning Multi-Scene Absolute Pose Regression with Transformers](https://mlsanity.com/paper/2103.11468/) (2021)
- [Direct-PoseNet: Absolute Pose Regression with Photometric Consistency](https://mlsanity.com/paper/2104.04073/) (2021)
- [DeepI2P: Image-to-Point Cloud Registration via Deep Classification](https://mlsanity.com/paper/2104.03501/) (2021)
- [Camera Pose Auto-Encoders for Improving Pose Regression](https://mlsanity.com/paper/2207.05530/) (2022)
- [Rethinking Efficient and Effective Point-based Networks for Event Camera Classification and Regression: EventMamba](https://mlsanity.com/paper/2405.06116/) (2024)
- [A Simple and Effective Point-based Network for Event Camera 6-DOFs Pose Relocalization](https://mlsanity.com/paper/2403.19412/) (2024)
- [Wide-Baseline Multi-Camera Calibration using Person Re-Identification](https://mlsanity.com/paper/2104.08568/) (2021)
- [Spacecraft Pose Estimation Based on Unsupervised Domain Adaptation and on a 3D-Guided Loss Combination](https://mlsanity.com/paper/2212.13415/) (2022)
- [Coarse-to-Fine Multi-Scene Pose Regression with Transformers](https://mlsanity.com/paper/2308.11783/) (2023)
- [Paying Attention to Activation Maps in Camera Pose Regression](https://mlsanity.com/paper/2103.11477/) (2021)
- [DistillPose: Lightweight Camera Localization Using Auxiliary Learning](https://mlsanity.com/paper/2108.03819/) (2021)
- [A Critical Analysis of Image-based Camera Pose Estimation Techniques](https://mlsanity.com/paper/2201.05816/) (2022)
- [Multi-Person 3D Pose Estimation from Multi-View Uncalibrated Depth Cameras](https://mlsanity.com/paper/2401.15616/) (2024)
- [Generative AI in Industrial Machine Vision -- A Review](https://mlsanity.com/paper/2408.10775/) (2024)
- [D-InLoc++: Indoor Localization in Dynamic Environments](https://mlsanity.com/paper/2209.10185/) (2022)
- [HyperPose: Hypernetwork-Infused Camera Pose Localization and an Extended Cambridge Landmarks Dataset](https://mlsanity.com/paper/2303.02610/) (2023)
- [Pose Estimation from Camera Images for Underwater Inspection](https://mlsanity.com/paper/2407.16961/) (2024)
- [MultiCam: On-the-fly Multi-Camera Pose Estimation Using Spatiotemporal Overlaps of Known Objects](https://mlsanity.com/paper/2603.22839/) (2026)
- [S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalization](https://mlsanity.com/paper/2205.05861/) (2022)
- [Differentiable Rendering for Pose Estimation in Proximity Operations](https://mlsanity.com/paper/2212.12668/) (2022)
- [Synfeal: A Data-Driven Simulator for End-to-End Camera Localization](https://mlsanity.com/paper/2305.18260/) (2023)
- [Online estimation of the hand-eye transformation from surgical scenes](https://mlsanity.com/paper/2306.02261/) (2023)
- [Relative Pose Regression with Pose Auto-Encoders: Enhancing Accuracy and Data Efficiency for Retail Applications](https://mlsanity.com/paper/2508.10933/) (2025)
- [CNN-Based Camera Pose Estimation and Localisation of Scan Images for Aircraft Visual Inspection](https://mlsanity.com/paper/2511.18702/) (2025)

## Abstract (arXiv)

Over the last two decades, deep learning has transformed the field of computer vision. Deep convolutional networks were successfully applied to learn different vision tasks such as image classification, image segmentation, object detection and many more. By transferring the knowledge learned by deep models on large generic datasets, researchers were further able to create fine-tuned models for other more specific tasks. Recently this idea was applied for regressing the absolute camera pose from an RGB image. Although the resulting accuracy was sub-optimal, compared to classic feature-based solutions, this effort led to a surge of learning-based pose estimation methods. Here, we review deep learning approaches for camera pose estimation. We describe key methods in the field and identify trends aiming at improving the original deep pose regression solution. We further provide an extensive cross-comparison of existing learning-based pose estimators, together with practical notes on their execution for reproducibility purposes. Finally, we discuss emerging solutions and potential future research directions.