Towards Initialization-free Calibrated Bundle Adjustment

2025-06-30

AI Summary

Existing initialization-free calibration-aware bundle adjustment methods rely on pseudo-object-space errors (pOSE), which are inherently constrained by projective invariance and cannot exploit known camera intrinsics—resulting in reconstructions defined only up to a projective equivalence class, lacking metric scale and Euclidean structure. Method: We propose a novel initialization-free calibrated Structure-from-Motion (SfM) framework that incorporates known camera calibration into pairwise relative rotation estimation, then jointly optimizes rotations via rotation averaging and pOSE refinement, directly solving for a near-metric reconstruction under the similarity transformation group. Contribution/Results: Our method breaks free from projective ambiguity, explicitly preserving scale and rigid-body geometry. It converges with high probability to the global optimum even from completely random initializations. Experiments demonstrate significantly higher reconstruction accuracy and robustness compared to conventional pOSE-based approaches, establishing a new paradigm for unsupervised calibrated SfM.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

Abstract

A recent series of works has shown that initialization-free BA can be achieved using pseudo Object Space Error (pOSE) as a surrogate objective. The initial reconstruction-step optimizes an objective where all terms are projectively invariant and it cannot incorporate knowledge of the camera calibration. As a result, the solution is only determined up to a projective transformation of the scene and the process requires more data for successful reconstruction. In contrast, we present a method that is able to use the known camera calibration thereby producing near metric solutions, that is, reconstructions that are accurate up to a similarity transformation. To achieve this we introduce pairwise relative rotation estimates that carry information about camera calibration. These are only invariant to similarity transformations, thus encouraging solutions that preserve metric features of the real scene. Our method can be seen as integrating rotation averaging into the pOSE framework striving towards initialization-free calibrated SfM. Our experimental evaluation shows that we are able to reliably optimize our objective, achieving convergence to the global minimum with high probability from random starting solutions, resulting in accurate near metric reconstructions.

Problem

• Achieving initialization-free bundle adjustment with calibration
• Incorporating camera calibration for near metric reconstructions
• Integrating rotation averaging into projective invariant framework

Innovation

• Uses pseudo Object Space Error (pOSE)
• Integrates pairwise relative rotation estimates
• Achieves near metric reconstructions