Event-based depth estimation with dense occlusion.

Occlusions pose a significant challenge to depth estimation in various fields, including automatic driving, remote sensing observation, and video surveillance. In this Letter, we propose a novel, to the best of our knowledge, depth estimation method for dense occlusion to estimate the depth behind occlusions. We design a comprehensive procedure using an event camera that consists of two steps: rough estimation and precise estimation.

Motion measurements of explosive shock waves based on an event camera.

Shock wave measurement is vital in assessing explosive power and designing warheads. To obtain satisfactory observation data of explosive shock waves, it is preferable for optical sensors to possess high-dynamic range and high-time resolution capabilities. In this paper, the event camera is first employed to observe explosive shock waves, leveraging its high dynamic range and low latency.

Two large-exposure-ratio image fusion by improved morphological segmentation.

The fusion of two large-exposure-ratio images, especially in the rocket launch field, is a challenging task because of fast-moving objects and differential features from daily scenes. Based on the large-exposure-ratio images, we propose a principle of halo formation at the boundaries of over-exposed areas. To avoid the halos in the fusion images, an improved morphological segmentation (IMS) method is developed to segment the over-exposed regions and boundaries.

Self-calibration of cameras using affine correspondences and known relative rotation angle.

This paper proposes a flexible method for camera self-calibration using affine correspondences and known relative rotation angle, which applies to the case that camera and inertial measurement unit (IMU) are tightly fixed. An affine correspondence provides two more constraints for the self-calibration problem than a traditional point correspondence, and the relative rotation angle can be derived from the IMU. Therefore, calibrating intrinsic camera parameters needs fewer correspondences, which can reduce the iterations and improve the algorithm's robustness in the random sample consensus framework.

Rotation alignment of a camera-IMU system using a single affine correspondence.

We propose an accurate and easy-to-implement method on rotation alignment of a camera-inertial measurement unit (IMU) system using only a single affine correspondence in the minimal case. The known initial rotation angles between the camera and IMU are utilized; thus, the alignment model can be formulated as a polynomial equation system based on homography constraints by expressing the rotation matrix in a first-order approximation. By solving the equation system, we can recover the rotation alignment parameters.

Relative Pose Estimation With a Single Affine Correspondence.

In this article, we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points, and we demonstrate efficient solvers for these cases. It is shown that under the planar motion assumption or with knowledge of a vertical direction, a single affine correspondence is sufficient to recover the relative camera pose. The four cases considered are two-view planar relative motion for calibrated cameras as a closed-form and least-squares solutions, a closed-form solution for unknown focal length, and the case of a known vertical direction.

Reconstruction-based 6D pose estimation for robotic assembly.

Pose estimation is important for many robotic applications including bin picking and robotic assembly and collaboration. However, robust and accurate estimation of the poses of industrial objects is a challenging task owing to the various object shapes and complex working environments. This paper presents a method of estimating the poses of narrow and elongated industrial objects with a low-cost RGB-D (depth and color) camera to guide the process of robotic assembly.

Self-calibration approach to stereo cameras with radial distortion based on epipolar constraint.

In this paper, we propose a self-calibration approach to stereo cameras with radial distortion from stereo image pairs of a common 3D scene. Based on the epipolar constraint in the stereo image pair, the intrinsic and extrinsic parameters of stereo cameras are estimated synchronously with a minimum number of nine image point correspondences. It is significant within a random sample consensus (RANSAC) scheme to cope with the outliers of feature matches efficiently and robustly.

Planar self-calibration for stereo cameras with radial distortion.

In this paper, we present a robust technique of stereo calibration using homography constraints. Our method is novel as stereo calibration is performed by solving a polynomial equation system including two radial distortion parameters, using a minimal number of five image point correspondences. This enables us to calibrate from only a pair of stereo images of a planar scene, and to provide the exact algebraic solution to the stereo calibration problem.