Learning Affine Correspondences by Integrating Geometric Constraints

Affine correspondences have received significant attention due to their benefits in tasks like image matching and pose estimation. Existing methods for extracting affine correspondences still have many limitations in terms of performance; thus, exploring a new paradigm is crucial. In this paper, we present a new pipeline designed for extracting accurate affine correspondences by integrating dense matching and geometric constraints. Specifically, a novel extraction framework is introduced, with the aid of dense matching and a novel keypoint scale and orientation estimator. For this purpose, we propose loss functions based on geometric constraints, which can effectively improve accuracy by supervising neural networks to learn feature geometry. The experimental show that the accuracy and robustness of our method outperform the existing ones in image matching tasks. To further demonstrate the effectiveness of the proposed method, we applied it to relative pose estimation. Affine correspondences extracted by our method lead to more accurate poses than the baselines on a range of real-world datasets. The code is available at https://github.com/stilcrad/DenseAffine.

Stereo Event-based, 6-DOF Pose Tracking for Uncooperative Spacecraft

Pose tracking of uncooperative spacecraft is an essential technology for space exploration and on-orbit servicing, which remains an open problem. Event cameras possess numerous advantages, such as high dynamic range, high temporal resolution, and low power consumption. These attributes hold the promise of overcoming challenges encountered by conventional cameras, including motion blur and extreme illumination, among others. To address the standard on-orbit observation missions, we propose a line-based pose tracking method for uncooperative spacecraft utilizing a stereo event camera. To begin with, we estimate the wireframe model of uncooperative spacecraft, leveraging the spatio-temporal consistency of stereo event streams for line-based reconstruction. Then, we develop an effective strategy to establish correspondences between events and projected lines of uncooperative spacecraft. Using these correspondences, we formulate the pose tracking as a continuous optimization process over 6-DOF motion parameters, achieved by minimizing event-line distances. Moreover, we construct a stereo event-based uncooperative spacecraft motion dataset, encompassing both simulated and real events. The proposed method is quantitatively evaluated through experiments conducted on our self-collected dataset, demonstrating an improvement in terms of effectiveness and accuracy over competing methods. The code will be open-sourced at https://github.com/Zibin6/SE6PT.

Camera Calibration using a Collimator System

Camera calibration is a crucial step in photogrammetry and 3D vision applications. In practical scenarios with a long working distance to cover a wide area, target-based calibration methods become complicated and inflexible due to site limitations. This paper introduces a novel camera calibration method using a collimator system, which can provide a reliable and controllable calibration environment for cameras with varying working distances. Based on the optical geometry of the collimator system, we prove that the relative motion between the target and camera conforms to the spherical motion model, reducing the original 6DOF relative motion to 3DOF pure rotation motion. Furthermore, a closed-form solver for multiple views and a minimal solver for two views are proposed for camera calibration. The performance of our method is evaluated in both synthetic and real-world experiments, which verify the feasibility of calibration using the collimator system and demonstrate that our method is superior to the state-of-the-art methods. Demo code is available at https://github.com/LiangSK98/CollimatorCalibration.