Fast and Accurate Camera Covariance Computation for Large 3D Reconstruction

TL;DR

This paper introduces a fast, accurate algorithm for computing camera uncertainties in large-scale 3D reconstructions, significantly improving efficiency without sacrificing accuracy.

Contribution

The authors present a novel algorithm leveraging sparsity in uncertainty propagation, enabling rapid and precise covariance computation for thousands of cameras.

Findings

Speeds up covariance computation by about ten times compared to previous methods.

Accurately computes uncertainties without approximations.

Effective for large and small reconstructions, scalable to any size.

Abstract

Estimating uncertainty of camera parameters computed in Structure from Motion (SfM) is an important tool for evaluating the quality of the reconstruction and guiding the reconstruction process. Yet, the quality of the estimated parameters of large reconstructions has been rarely evaluated due to the computational challenges. We present a new algorithm which employs the sparsity of the uncertainty propagation and speeds the computation up about ten times \wrt previous approaches. Our computation is accurate and does not use any approximations. We can compute uncertainties of thousands of cameras in tens of seconds on a standard PC. We also demonstrate that our approach can be effectively used for reconstructions of any size by applying it to smaller sub-reconstructions.