# RPNet: an End-to-End Network for Relative Camera Pose Estimation

arXiv: https://arxiv.org/abs/1809.08402 · HTML: https://mlsanity.com/paper/1809.08402/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2018.

## Built on by (most cited first, 30 of 31)

- [SPARF: Neural Radiance Fields from Sparse and Noisy Poses](https://mlsanity.com/paper/2211.11738/) (2022)
- [Map-free Visual Relocalization: Metric Pose Relative to a Single Image](https://mlsanity.com/paper/2210.05494/) (2022)
- [Reloc3r: Large-Scale Training of Relative Camera Pose Regression for Generalizable, Fast, and Accurate Visual Localization](https://mlsanity.com/paper/2412.08376/) (2024)
- [Wide-Baseline Relative Camera Pose Estimation with Directional Learning](https://mlsanity.com/paper/2106.03336/) (2021)
- [Extreme Rotation Estimation using Dense Correlation Volumes](https://mlsanity.com/paper/2104.13530/) (2021)
- [Map-Relative Pose Regression for Visual Re-Localization](https://mlsanity.com/paper/2404.09884/) (2024)
- [Planar Surface Reconstruction from Sparse Views](https://mlsanity.com/paper/2103.14644/) (2021)
- [The 8-Point Algorithm as an Inductive Bias for Relative Pose Prediction by ViTs](https://mlsanity.com/paper/2208.08988/) (2022)
- [Unifying Correspondence, Pose and NeRF for Pose-Free Novel View Synthesis from Stereo Pairs](https://mlsanity.com/paper/2312.07246/) (2023)
- [Self-supervised Geometric Perception](https://mlsanity.com/paper/2103.03114/) (2021)
- [Virtual Correspondence: Humans as a Cue for Extreme-View Geometry](https://mlsanity.com/paper/2206.08365/) (2022)
- [NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction](https://mlsanity.com/paper/2211.16799/) (2022)
- [FAR: Flexible, Accurate and Robust 6DoF Relative Camera Pose Estimation](https://mlsanity.com/paper/2403.03221/) (2024)
- [Deep Learning Reforms Image Matching: A Survey and Outlook](https://mlsanity.com/paper/2506.04619/) (2025)
- [Leveraging Image Matching Toward End-to-End Relative Camera Pose Regression](https://mlsanity.com/paper/2211.14950/) (2022)
- [PlaneRecTR++: Unified Query Learning for Joint 3D Planar Reconstruction and Pose Estimation](https://mlsanity.com/paper/2307.13756/) (2023)
- [SRPose: Two-view Relative Pose Estimation with Sparse Keypoints](https://mlsanity.com/paper/2407.08199/) (2024)
- [GSVisLoc: Generalizable Visual Localization for Gaussian Splatting Scene Representations](https://mlsanity.com/paper/2508.18242/) (2025)
- [RelMobNet: End-to-end relative camera pose estimation using a robust two-stage training](https://mlsanity.com/paper/2202.12838/) (2022)
- [CGA-PoseNet: Camera Pose Regression via a 1D-Up Approach to Conformal Geometric Algebra](https://mlsanity.com/paper/2302.05211/) (2023)
- [A Lightweight Domain Adaptive Absolute Pose Regressor Using Barlow Twins Objective](https://mlsanity.com/paper/2211.10963/) (2022)
- [Self-supervised learning of object pose estimation using keypoint prediction](https://mlsanity.com/paper/2302.07360/) (2023)
- [HyperPose: Hypernetwork-Infused Camera Pose Localization and an Extended Cambridge Landmarks Dataset](https://mlsanity.com/paper/2303.02610/) (2023)
- [Multi-task Learning for Camera Calibration](https://mlsanity.com/paper/2211.12432/) (2022)
- [EAR-Net: Pursuing End-to-End Absolute Rotations from Multi-View Images](https://mlsanity.com/paper/2310.10051/) (2023)
- [Map-Free Visual Relocalization Enhanced by Instance Knowledge and Depth Knowledge](https://mlsanity.com/paper/2408.13085/) (2024)
- [Leveraging Spatial Attention and Edge Context for Optimized Feature Selection in Visual Localization](https://mlsanity.com/paper/2410.12240/) (2024)
- [iGaussian: Real-Time Camera Pose Estimation via Feed-Forward 3D Gaussian Splatting Inversion](https://mlsanity.com/paper/2511.14149/) (2025)
- [LP-LLM: End-to-End Real-World Degraded License Plate Text Recognition via Large Multimodal Models](https://mlsanity.com/paper/2601.09116/) (2026)
- [InCaRPose: In-Cabin Relative Camera Pose Estimation Model and Dataset](https://mlsanity.com/paper/2604.03814/) (2026)

## Abstract (arXiv)

This paper addresses the task of relative camera pose estimation from raw image pixels, by means of deep neural networks. The proposed RPNet network takes pairs of images as input and directly infers the relative poses, without the need of camera intrinsic/extrinsic. While state-of-the-art systems based on SIFT + RANSAC, are able to recover the translation vector only up to scale, RPNet is trained to produce the full translation vector, in an end-to-end way. Experimental results on the Cambridge Landmark dataset show very promising results regarding the recovery of the full translation vector. They also show that RPNet produces more accurate and more stable results than traditional approaches, especially for hard images (repetitive textures, textureless images, etc). To the best of our knowledge, RPNet is the first attempt to recover full translation vectors in relative pose estimation.