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

2026-04-26

AI Summary

This work addresses the computational inefficiency of conventional bundle adjustment in multi-camera systems, which jointly optimizes camera poses and 3D points. By leveraging the generalized camera model, the authors introduce, for the first time, a purely pose-based geometric constraint that implicitly encodes 3D points through two reference observations and their associated poses, thereby entirely eliminating 3D point parameters from the optimization. The resulting formulation optimizes only camera poses, significantly enhancing computational efficiency while maintaining or even improving pose estimation accuracy. Experimental results on both synthetic and real-world datasets demonstrate that the proposed method consistently outperforms traditional bundle adjustment in terms of speed and accuracy.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

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.

Problem

• multi-camera systems
• bundle adjustment
• computational efficiency
• pose optimization
• feature redundancy

Innovation

• pose-only constraint
• multi-camera system
• bundle adjustment
• generalized camera model
• efficient pose optimization