The field of 3D pose estimation and reconstruction is rapidly advancing, with a focus on developing more accurate and efficient methods for estimating the pose of objects and reconstructing 3D scenes. Recent research has explored the use of novel techniques such as meta-learning, diffusion processes, and transformer architectures to improve the accuracy and robustness of pose estimation and reconstruction algorithms. These advances have the potential to enable a wide range of applications, including robotics, augmented reality, and computer vision. Notable papers in this area include: An uncertainty-aware framework for data-efficient multi-view animal pose estimation, which proposes a comprehensive framework for pose estimation that combines novel training and post-processing techniques. HccePose(BF) predicts 3D coordinates of both the object's front and back surfaces to create ultra-dense 2D-3D correspondences, effectively enhancing pose estimation accuracy. PointMAC proposes a meta-learned framework for robust test-time adaptation in point cloud completion, enabling sample-specific refinement without requiring additional supervision. MonoSE(3)-Diffusion formulates markerless, image-based robot pose estimation as a conditional denoising diffusion process, improving the network generalization capability. WorldMirror presents an all-in-one, feed-forward model for versatile 3D geometric prediction tasks, flexibly integrating diverse geometric priors and generating multiple 3D representations. DKPMV achieves dense keypoint-level fusion using only multi-view RGB images as input, leveraging dense multi-view keypoint geometry information. Beyond 'Templates' proposes a unified, category-agnostic framework that simultaneously predicts 6D pose, size, and dense shape from a single RGB-D image, without requiring templates, CAD models, or category labels at test time.