# A Pose-only Geometric Constraint for Multi-Camera Pose Adjustment

arXiv: https://arxiv.org/abs/2604.23704 · HTML: https://mlsanity.com/paper/2604.23704/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 67 references that are in the map.
- First posted on arXiv 26 Apr 2026.

## Builds on (most cited first, 23 of 23)

- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras](https://mlsanity.com/paper/1708.07878/) (2017)
- [R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Cameras](https://mlsanity.com/paper/2308.14713/) (2023)
- [Project AutoVision: Localization and 3D Scene Perception for an Autonomous Vehicle with a Multi-Camera System](https://mlsanity.com/paper/1809.05477/) (2018)
- [AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot](https://mlsanity.com/paper/2007.01813/) (2020)
- [A Pose-only Solution to Visual Reconstruction and Navigation](https://mlsanity.com/paper/2103.01530/) (2021)
- [Stochastic Bundle Adjustment for Efficient and Scalable 3D Reconstruction](https://mlsanity.com/paper/2008.00446/) (2020)
- [Square Root Bundle Adjustment for Large-Scale Reconstruction](https://mlsanity.com/paper/2103.01843/) (2021)
- [Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares](https://mlsanity.com/paper/2305.09527/) (2023)
- [Redesigning SLAM for Arbitrary Multi-Camera Systems](https://mlsanity.com/paper/2003.02014/) (2020)
- [MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment](https://mlsanity.com/paper/2112.01349/) (2021)
- [Affine Correspondences between Multi-Camera Systems for Relative Pose Estimation](https://mlsanity.com/paper/2306.12996/) (2023)
- [Rotation-Only Bundle Adjustment](https://mlsanity.com/paper/2011.11724/) (2020)
- [MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System](https://mlsanity.com/paper/1610.07336/) (2016)
- [BAMF-SLAM: Bundle Adjusted Multi-Fisheye Visual-Inertial SLAM Using Recurrent Field Transforms](https://mlsanity.com/paper/2306.01173/) (2023)
- [Equivalent Constraints for Two-View Geometry: Pose Solution/Pure Rotation Identification and 3D Reconstruction](https://mlsanity.com/paper/1810.05863/) (2018)
- [Camera Calibration using a Collimator System](https://mlsanity.com/paper/2409.20034/) (2024)
- [Balancing the Budget: Feature Selection and Tracking for Multi-Camera Visual-Inertial Odometry](https://mlsanity.com/paper/2109.05975/) (2021)
- [MAVIS: Multi-Camera Augmented Visual-Inertial SLAM using SE2(3) Based Exact IMU Pre-integration](https://mlsanity.com/paper/2309.08142/) (2023)
- [Design and Evaluation of a Generic Visual SLAM Framework for Multi-Camera Systems](https://mlsanity.com/paper/2210.07315/) (2022)
- [Optimal Pose Guidance for Stereo Calibration in 3D Deformation Measurement](https://mlsanity.com/paper/2511.18317/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Geometric Framework for Absolute Pose and Velocity Estimation with Event Cameras](https://mlsanity.com/paper/2606.09139/) (2026)

## Abstract (arXiv)

Multi-camera systems offer rich observation capabilities for visual navigation and 3D scene reconstruction; however, the resulting feature redundancy often compromises computational efficiency. This challenge is particularly pronounced during bundle adjustment, where the non-linear optimization of both system poses and scene points incurs substantial computational overhead. To address this challenge, this paper introduces a pose-only geometric constraint for multi-camera systems and proposes a corresponding pose adjustment algorithm. Specifically, we use generalized camera model to establish a unified representation of the multi-camera system. Building upon this model, we formulate the multi-camera pose-only constraint, which implicitly represents a 3D scene point using two base observations and their associated poses, thereby achieving a pose-only representation of the projection geometry. Subsequently, we introduce a multi-camera pose adjustment algorithm that eliminates 3D points from the parameter space, thereby achieving efficient and focused pose optimization. Experimental results on both synthetic and real-world datasets demonstrate that the proposed algorithm outperforms baseline bundle adjustment methods in computational efficiency, while maintaining or even improving pose estimation accuracy.