PnP-Net: A hybrid Perspective-n-Point Network
This work addresses camera pose estimation in computer vision, particularly for scenarios with mismatched correspondences, representing an incremental improvement over classical methods.
The paper tackled the robust Perspective-n-Point (PnP) problem for camera pose estimation by proposing PnP-Net, a hybrid approach combining deep learning with model-based algorithms, which achieved accurate pose estimation under correspondence errors and noise with low, fixed computational complexity.
We consider the robust Perspective-n-Point (PnP) problem using a hybrid approach that combines deep learning with model based algorithms. PnP is the problem of estimating the pose of a calibrated camera given a set of 3D points in the world and their corresponding 2D projections in the image. In its more challenging robust version, some of the correspondences may be mismatched and must be efficiently discarded. Classical solutions address PnP via iterative robust non-linear least squares method that exploit the problem's geometry but are either inaccurate or computationally intensive. In contrast, we propose to combine a deep learning initial phase followed by a model-based fine tuning phase. This hybrid approach, denoted by PnP-Net, succeeds in estimating the unknown pose parameters under correspondence errors and noise, with low and fixed computational complexity requirements. We demonstrate its advantages on both synthetic data and real world data.