Optimal DLT-based Solutions for the Perspective-n-Point
This provides a more efficient solution for camera pose estimation in computer vision, though it appears incremental as it builds on existing DLT methods.
The authors tackled the perspective-n-point (PnP) problem by proposing a modified normalized direct linear transform (DLT) algorithm with analytical weighting, which improves performance and runtime compared to popular methods like EPnP and CPnP, approaching optimal results at lower computational cost.
We propose a modified normalized direct linear transform (DLT) algorithm for solving the perspective-n-point (PnP) problem with much better behavior than the conventional DLT. The modification consists of analytically weighting the different measurements in the linear system with a negligible increase in computational load. Our approach exhibits clear improvements -- in both performance and runtime -- when compared to popular methods such as EPnP, CPnP, RPnP, and OPnP. Our new non-iterative solution approaches that of the true optimal found via Gauss-Newton optimization, but at a fraction of the computational cost. Our optimal DLT (oDLT) implementation, as well as the experiments, are released in open source.