# A Pose-only Solution to Visual Reconstruction and Navigation

arXiv: https://arxiv.org/abs/2103.01530 · HTML: https://mlsanity.com/paper/2103.01530/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 58 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Robust Camera Location Estimation by Convex Programming](https://mlsanity.com/paper/1412.0165/) (2014)
- [Baseline Desensitizing In Translation Averaging](https://mlsanity.com/paper/1901.00643/) (2019)
- [ShapeFit and ShapeKick for Robust, Scalable Structure from Motion](https://mlsanity.com/paper/1608.02165/) (2016)
- [Linear Global Translation Estimation with Feature Tracks](https://mlsanity.com/paper/1503.01832/) (2015)
- [Equivalent Constraints for Two-View Geometry: Pose Solution/Pure Rotation Identification and 3D Reconstruction](https://mlsanity.com/paper/1810.05863/) (2018)

## Built on by (most cited first, 20 of 20)

- [Global Structure-from-Motion Revisited](https://mlsanity.com/paper/2407.20219/) (2024)
- [GaussNav: Gaussian Splatting for Visual Navigation](https://mlsanity.com/paper/2403.11625/) (2024)
- [Light3R-SfM: Towards Feed-forward Structure-from-Motion](https://mlsanity.com/paper/2501.14914/) (2025)
- [RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo](https://mlsanity.com/paper/2307.10233/) (2023)
- [EC-SfM: Efficient Covisibility-based Structure-from-Motion for Both Sequential and Unordered Images](https://mlsanity.com/paper/2302.10544/) (2023)
- [JOG3R: Towards 3D-Consistent Video Generators](https://mlsanity.com/paper/2501.01409/) (2025)
- [Regist3R: Incremental Registration with Stereo Foundation Model](https://mlsanity.com/paper/2504.12356/) (2025)
- [Six-Point Method for Multi-Camera Systems with Reduced Solution Space](https://mlsanity.com/paper/2402.18066/) (2024)
- [Towards Real-World Aerial Vision Guidance with Categorical 6D Pose Tracker](https://mlsanity.com/paper/2401.04377/) (2024)
- [An Accurate and Real-time Relative Pose Estimation from Triple Point-line Images by Decoupling Rotation and Translation](https://mlsanity.com/paper/2403.11639/) (2024)
- [Linear Relative Pose Estimation Founded on Pose-only Imaging Geometry](https://mlsanity.com/paper/2401.13357/) (2024)
- [PO-MSCKF: An Efficient Visual-Inertial Odometry by Reconstructing the Multi-State Constrained Kalman Filter with the Pose-only Theory](https://mlsanity.com/paper/2407.01888/) (2024)
- [A Pose-only Geometric Constraint for Multi-Camera Pose Adjustment](https://mlsanity.com/paper/2604.23704/) (2026)
- [MGSfM: Multi-Camera Geometry Driven Global Structure-from-Motion](https://mlsanity.com/paper/2507.03306/) (2025)
- [Towards Rotation-only Imaging Geometry: Rotation Estimation](https://mlsanity.com/paper/2511.12415/) (2025)
- [POPL-KF: A Pose-Only Geometric Representation-Based Kalman Filter for Point-Line-Based Visual-Inertial Odometry](https://mlsanity.com/paper/2602.06425/) (2026)
- [HETA++: Global Structure-from-Motion with Hybrid Explicit Translation Averaging](https://mlsanity.com/paper/2607.15912/) (2026)
- [Triangulation-Free Bundle Adjustment with Graduated Non-Convexity for Camera Pose Refinement from Coarse Priors](https://mlsanity.com/paper/2608.21008/) (2026)
- [Robust Global Structure-from-Motion via View Graph Pruning](https://mlsanity.com/paper/2608.22054/) (2026)
- [Robust Structureless Monocular Visual Inertial Initialization Exploiting Line Features and Vanishing Points](https://mlsanity.com/paper/2609.21186/) (2026)

## Abstract (arXiv)

Visual navigation and three-dimensional (3D) scene reconstruction are essential for robotics to interact with the surrounding environment. Large-scale scenes and critical camera motions are great challenges facing the research community to achieve this goal. We raised a pose-only imaging geometry framework and algorithms that can help solve these challenges. The representation is a linear function of camera global translations, which allows for efficient and robust camera motion estimation. As a result, the spatial feature coordinates can be analytically reconstructed and do not require nonlinear optimization. Experiments demonstrate that the computational efficiency of recovering the scene and associated camera poses is significantly improved by 2-4 orders of magnitude. This solution might be promising to unlock real-time 3D visual computing in many forefront applications.