Averaging Essential and Fundamental Matrices in Collinear Camera Settings
This addresses a specific drawback in 3D reconstruction for computer vision applications, particularly in challenging collinear setups, but is incremental as it builds on existing global methods.
The paper tackles the problem of global Structure from Motion being sensitive to collinear camera settings by analyzing and developing algorithms for averaging bifocal tensors in such scenarios, achieving state-of-the-art results on benchmarks including autonomous car datasets and unordered image collections.
Global methods to Structure from Motion have gained popularity in recent years. A significant drawback of global methods is their sensitivity to collinear camera settings. In this paper, we introduce an analysis and algorithms for averaging bifocal tensors (essential or fundamental matrices) when either subsets or all of the camera centers are collinear. We provide a complete spectral characterization of bifocal tensors in collinear scenarios and further propose two averaging algorithms. The first algorithm uses rank constrained minimization to recover camera matrices in fully collinear settings. The second algorithm enriches the set of possibly mixed collinear and non-collinear cameras with additional, "virtual cameras," which are placed in general position, enabling the application of existing averaging methods to the enriched set of bifocal tensors. Our algorithms are shown to achieve state of the art results on various benchmarks that include autonomous car datasets and unordered image collections in both calibrated and unclibrated settings.