The field of 3D reconstruction is moving towards more robust and accurate methods, particularly in handling sparse and unordered image sets. Recent developments have focused on improving the accuracy and efficiency of 3D reconstruction algorithms, including the use of neural implicit models, explicit point-cloud-based approaches, and hybrid frameworks. Additionally, there is a growing interest in uncertainty quantification, with researchers working on developing frameworks that can provide per-point accuracy credentials for 3D point clouds. Noteworthy papers include: Uncertainty Quantification Framework for Aerial and UAV Photogrammetry through Error Propagation, which presents a novel method for estimating uncertainty in the MVS stage, and DCHM: Depth-Consistent Human Modeling for Multiview Detection, which proposes a framework for consistent depth estimation and multiview fusion in global coordinates.