The field of visual SLAM and image matching is currently experiencing significant developments, with a focus on improving the accuracy and robustness of pose estimation and 3D reconstruction in dynamic environments. Researchers are exploring innovative approaches, such as leveraging depth variance constraints and autoencoder-preprocessed genetic keypoints resampling, to handle dynamic scenes. Additionally, efficient match summarization schemes and ego-centric metrics are being proposed to speed up robust two-view estimation and improve the reliability of matches in 3D multi-object tracking. Noteworthy papers in this area include GeneA-SLAM2, which proposes a robust and efficient system for dynamic SLAM, and Contour Errors, which introduces an ego-centric metric for reliable 3D multi-object tracking. SupeRANSAC is also a notable contribution, providing a unified RANSAC pipeline for consistent accuracy across various vision tasks.