The field of computer vision and 3D measurement is moving towards more accurate and efficient methods for tasks such as stereo calibration, camera pose estimation, and anomaly detection. Researchers are exploring the use of deep learning techniques, such as convolutional neural networks, to improve the accuracy and robustness of these methods. Additionally, there is a focus on developing methods that can work in real-world environments, such as outdoor settings with varying lighting conditions. Noteworthy papers include: Unsupervised Multi-View Visual Anomaly Detection via Progressive Homography-Guided Alignment, which introduces a novel framework for unsupervised visual anomaly detection from multi-view images, and CNN-Based Camera Pose Estimation and Localisation of Scan Images for Aircraft Visual Inspection, which proposes an on-site method for estimating a camera's pose with respect to an aircraft. ShelfRectNet: Single View Shelf Image Rectification with Homography Estimation is also notable for its deep learning framework that predicts a 4-point parameterized homography matrix to rectify shelf images captured from arbitrary angles.