The field of 3D registration and motion estimation is rapidly advancing, with a focus on developing robust and efficient methods for aligning and tracking 3D data. Recent developments have seen a shift towards leveraging geometric and spatial information to improve registration accuracy and speed. Notably, researchers are exploring the use of novel frameworks and techniques, such as deformable registration, dual-space filtering, and geometric overlapping guided rotation search, to address challenges in registration and motion estimation. These innovative approaches have shown promising results in improving registration accuracy, reducing computational overhead, and enhancing robustness to noise and outliers. Noteworthy papers include: Gaussian Primitive Optimized Deformable Retinal Image Registration, which introduces a novel iterative framework for deformable registration. DualReg: Dual-Space Filtering and Reinforcement for Rigid Registration, which proposes a dual-space paradigm for rigid registration. Robust Point Cloud Registration via Geometric Overlapping Guided Rotation Search, which presents a geometric maximum overlapping registration framework. Minimal Solvers for Full DoF Motion Estimation from Asynchronous Tracks, which develops minimal solvers for motion estimation from asynchronous tracks. Surfel-based 3D Registration with Equivariant SE(3) Features, which proposes a novel surfel-based pose learning regression approach.