Accurate and linear time pose estimation from points and lines
European Conference on Computer Vision (ECCV), 2016
Alexander Vakhitov, Jan Funke, Francesc Moreno-Noguer
The Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3D-to-2D point correspondences. There are situations, though, where PnP solutions are prone to fail be- cause feature point correspondences cannot be reliably estimated (e.g. scenes with repetitive patterns or with low texture). In such scenarios, one can still exploit alternative geometric entities, such as lines, yielding the so-called Perspective-n-Line (PnL) algorithms. Unfortunately, exist- ing PnL solutions are not as accurate and efficient as their point-based counterparts. In this paper we propose a novel approach to introduce 3D-to-2D line correspondences into a PnP formulation, allowing to si- multaneously process points and lines. For this purpose we introduce an algebraic line error that can be formulated as linear constraints on the line endpoints, even when these are not directly observable. These constraints can then be naturally integrated within the linear formula- tions of two state-of-the-art point-based algorithms, the OPnP and the EPnP, allowing them to indistinctly handle points, lines, or a combination of them. Exhaustive experiments show that the proposed formulation brings remarkable boost in performance compared to only point or only line based solutions, with a negligible computational over- head compared to the original OPnP and EPnP.
Comparison of pose estimation results with OPnPL (left) and OPnP (right) in a scenario with a lack of reliable feature points. Blue points and solid line segments are detected in the image, and green dashed line segments are the model reference lines reprojected using the estimated pose.
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Paper
Vakhitov, Alexander and Funke, Jan and Moreno-Noguer, Francesc Accurate and linear time pose estimation from points and lines, European Conference on Computer Vision, 2016, pp. 583–599 pdf bib