The field of computer vision is witnessing significant advancements in keypoint matching and pose estimation. Recent research has focused on developing more accurate and efficient methods for estimating the pose of objects from images. One notable direction is the use of deep learning-based approaches, which have shown impressive results in improving the accuracy of keypoint matching and pose estimation. Another area of interest is the development of novel loss functions and training methods that can improve the robustness and generalizability of these models. Additionally, there is a growing interest in exploring new paradigms for keypoint learning, such as incremental learning and recurrent feature mining. These advancements have the potential to enable more accurate and efficient pose estimation, which is crucial for various applications, including robotics, augmented reality, and computer-aided design. Noteworthy papers include:

• Normalized Matching Transformer, which presents a state-of-the-art approach for sparse keypoint matching between pairs of images, outperforming current state-of-the-art approaches on several datasets.
• Co-op, which proposes a novel method for accurately and robustly estimating the 6DoF pose of objects unseen during training from a single RGB image, achieving state-of-the-art accuracy on several datasets.
• Structure-Aware Correspondence Learning for Relative Pose Estimation, which proposes a novel method for relative pose estimation that can naturally estimate 3D-3D correspondences for unseen objects without explicit feature matching.
• Incremental Object Keypoint Learning, which explores a novel keypoint learning paradigm that can effectively mitigate the catastrophic forgetting of old keypoints and achieve a positive transfer beyond anti-forgetting.
• Recurrent Feature Mining and Keypoint Mixup Padding for Category-Agnostic Pose Estimation, which proposes a novel framework that recurrently mines fine-grained and structure-aware features from both support and query images, achieving state-of-the-art results on a large-scale dataset.