LOSTU: Fast, Scalable, and Uncertainty-Aware Triangulation
This work addresses triangulation for 3D reconstruction in computer vision, offering a faster and uncertainty-aware solution that is incremental over existing methods.
The paper tackles the problem of triangulation in computer vision by proposing LOSTU, a non-iterative and scalable method that provides maximum likelihood estimates even with camera errors, resulting in substantially faster performance than optimization schemes while maintaining comparable precision.
This work proposes a non-iterative, scalable, and statistically optimal way to triangulate called \texttt{LOSTU}. Unlike triangulation algorithms that minimize the reprojection ($L_2$) error, LOSTU will still provide the maximum likelihood estimate when there are errors in camera pose or parameters. This generic framework is used to contextualize other triangulation methods like the direct linear transform (DLT) or the midpoint. Synthetic experiments show that LOSTU can be substantially faster than using uncertainty-aware Levenberg-Marquardt (or similar) optimization schemes, while providing results of comparable precision. Finally, LOSTU is implemented in sequential reconstruction in conjunction with uncertainty-aware pose estimation, where it yields better reconstruction metrics.