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.
The Perspective-n-Point problem aims to estimate the relative pose between a calibrated monocular camera and a known 3D model, by aligning pairs of 2D captured image points to their corresponding 3D points in the model. We suggest an algorithm that runs on weak IoT devices in real-time but still provides provable theoretical guarantees for both running time and correctness. Existing solvers provide only one of these requirements. Our main motivation was to turn the popular DJI's Tello Drone (<90gr, <\$100) into an autonomous drone that navigates in an indoor environment with no external human/…
The University of Adelaide（阿德莱德大学）；Shenzhen University（深圳大学）
Deep networks excel in learning patterns from large amounts of data. On the other hand, many geometric vision tasks are specified as optimization problems. To seamlessly combine deep learning and geometric vision, it is vital to perform learning and geometric optimization end-to-end. Towards this aim, we present BPnP, a novel network module that backpropagates gradients through a Perspective-n-Points (PnP) solver to guide parameter updates of a neural network. Based on implicit differentiation, we show that the gradients of a "self-contained" PnP solver can be derived accurately and efficientl…
GeVI-SLAM: Gravity-Enhanced Stereo Visua Inertial SLAM for Underwater Robots
School of Data Science, Chinese University of Hong Kong, Shenzhen, China（数据科学学院，香港中文大学（深圳））；School of Life Sciences, Chinese University of Hong Kong, China（生命科学学院，香港中文大学）
LATINO-PRO: LAtent consisTency INverse sOlver with PRompt Optimization
CNRS（法国国家科学研究中心）；Univ. Bordeaux（波尔多大学）；INRIA（法国国家信息与自动化研究所）；Bordeaux INP（波尔多国立高等工程技术学院）；Heriot-Watt University（赫瑞瓦特大学）；Maxwell Institute for Mathematical Sciences（麦克斯韦数学科学研究所）
Perspective-n-Point-and-Line (P$n$PL) algorithms aim at fast, accurate, and robust camera localization with respect to a 3D model from 2D-3D feature correspondences, being a major part of modern robotic and AR/VR systems. Current point-based pose estimation methods use only 2D feature detection uncertainties, and the line-based methods do not take uncertainties into account. In our setup, both 3D coordinates and 2D projections of the features are considered uncertain. We propose PnP(L) solvers based on EPnP and DLS for the uncertainty-aware pose estimation. We also modify motion-only bundle ad…
Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching
Tsinghua University（清华大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；New York University Abu Dhabi（纽约大学阿布扎比分校）；Wayne State University（韦恩州立大学）
CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination
School of Data Science, Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）；Academy of Mathematics and Systems Science, Chinese Academy of Sciences（中国科学院数学与系统科学研究院）
Australian National University（澳大利亚国立大学）；Australian Centre for Robotic Vision（澳大利亚机器人视觉中心）；Baidu Research（百度研究院）
Conventional absolute camera pose via a Perspective-n-Point (PnP) solver often assumes that the correspondences between 2D image pixels and 3D points are given. When the correspondences between 2D and 3D points are not known a priori, the task becomes the much more challenging blind PnP problem. This paper proposes a deep CNN model which simultaneously solves for both the 6-DoF absolute camera pose and 2D--3D correspondences. Our model comprises three neural modules connected in sequence. First, a two-stream PointNet-inspired network is applied directly to both the 2D image keypoints and the 3…
Australian National University（澳大利亚国立大学）；Data61 – CSIRO（澳大利亚联邦科学与工业研究组织 Data61）
Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications. Perspective-n-Point (PnP) solvers are routinely used for camera pose estimation, provided that a good quality set of 2D-3D feature correspondences are known beforehand. However, finding optimal correspondences between 2D key-points and a 3D point-set is non-trivial, especially when only geometric (position) information is known. Existing approaches to the simultaneous pose and correspondence problem use local optimisation, and are therefo…
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；TU Kaiserslautern（凯泽斯劳滕工业大学）；Technische Universität München（慕尼黑工业大学）；Google（谷歌）
Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replaced sparse templates. Dense methods also improved pose estimation in the presence of occlusion. More recently researchers have shown improvements by learning object fragments as segmentation. In this work, we present a discrete descriptor, which can represent the object surface densely. By incorporating a hierarchical binary grouping, we can encode the object surface very efficiently. Moreover, we propose a coarse to f…
SRI International（SRI国际）；University of Pennsylvania（宾夕法尼亚大学）
Correspondence is a ubiquitous problem in computer vision and graph matching has been a natural way to formalize correspondence as an optimization problem. Recently, graph matching solvers have included higher-order terms representing affinities beyond the unary and pairwise level. Such higher-order terms have a particular appeal for geometric constraints that include three or more correspondences like the PnP 2D-3D pose problems. In this paper, we address the problem of finding correspondences in the absence of unary or pairwise constraints as it emerges in problems where unary appearance sim…
