Correspondence Based Method

Correspondence-based methods aim to estimate the pose and/or geometry of objects by establishing correspondences between points in 2D images and their 3D counterparts. Current research focuses on improving robustness and efficiency, particularly for challenging scenarios like symmetric objects and sparse data, through techniques such as probabilistic coordinate representations, progressive keypoint localization with graph neural networks, and novel symmetry-aware encodings. These advancements are driving improvements in applications like object pose estimation, 3D reconstruction, and image registration, leading to more accurate and reliable results in computer vision and related fields.