CvxPnPL: A Unified Convex Solution to the Absolute Pose Estimation Problem from Point and Line Correspondences
This work addresses a specific problem in computer vision for researchers and practitioners needing robust pose estimation from mixed features, representing an incremental improvement by unifying and relaxing existing methods.
The paper tackles the problem of estimating 3D pose from mixed 2D-3D point and line correspondences (PnPL) by developing a convex method that unifies contributions into a QCQP and relaxes it into an SDP, enabling handling of ambiguous configurations and recovering a finite number of solutions. Experiments show results competitive with state-of-the-art methods while offering flexibility in solving for arbitrary numbers of points and lines.
We present a new convex method to estimate 3D pose from mixed combinations of 2D-3D point and line correspondences, the Perspective-n-Points-and-Lines problem (PnPL). We merge the contributions of each point and line into a unified Quadratic Constrained Quadratic Problem (QCQP) and then relax it into a Semi Definite Program (SDP) through Shor's relaxation. This makes it possible to gracefully handle mixed configurations of points and lines. Furthermore, the proposed relaxation allows us to recover a finite number of solutions under ambiguous configurations. In such cases, the 3D pose candidates are found by further enforcing geometric constraints on the solution space and then retrieving such poses from the intersections of multiple quadrics. Experiments provide results in line with the best performing state of the art methods while providing the flexibility of solving for an arbitrary number of points and lines.