DynaWeightPnP: Toward global real-time 3D-2D solver in PnP without correspondences

• URL: http://arxiv.org/abs/2409.18457v1
• Date: Fri, 27 Sep 2024 05:31:33 GMT
• Title: DynaWeightPnP: Toward global real-time 3D-2D solver in PnP without correspondences
• Authors: Jingwei Song, Maani Ghaffari,
• Abstract summary: This paper addresses a special Perspective-n-Point (Weight) problem: estimating the optimal pose to align 3D and 2D shapes in real-time without correspondences. Experiments were conducted on a typical case, that is, a 3D-2D centerline registration task within Endovascular Image-Guided Interventions. Results demonstrated that the proposed algorithm achieves registration processing rates of 60 Hz (without post-refinement) and 31 (with post-refinement) with competitive accuracy comparable to existing methods.
• Score: 7.191124861153032
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses a special Perspective-n-Point (PnP) problem: estimating the optimal pose to align 3D and 2D shapes in real-time without correspondences, termed as correspondence-free PnP. While several studies have focused on 3D and 2D shape registration, achieving both real-time and accurate performance remains challenging. This study specifically targets the 3D-2D geometric shape registration tasks, applying the recently developed Reproducing Kernel Hilbert Space (RKHS) to address the "big-to-small" issue. An iterative reweighted least squares method is employed to solve the RKHS-based formulation efficiently. Moreover, our work identifies a unique and interesting observability issue in correspondence-free PnP: the numerical ambiguity between rotation and translation. To address this, we proposed DynaWeightPnP, introducing a dynamic weighting sub-problem and an alternative searching algorithm designed to enhance pose estimation and alignment accuracy. Experiments were conducted on a typical case, that is, a 3D-2D vascular centerline registration task within Endovascular Image-Guided Interventions (EIGIs). Results demonstrated that the proposed algorithm achieves registration processing rates of 60 Hz (without post-refinement) and 31 Hz (with post-refinement) on modern single-core CPUs, with competitive accuracy comparable to existing methods. These results underscore the suitability of DynaWeightPnP for future robot navigation tasks like EIGIs.

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