Choosing suitable data representations is one of the most critical topics when installing machine learning on 3D data. This talk discusses several recent works on utilizing uncertainty as a lens to develop suitable data representations and formulations for geometry reconstruction and understanding. We will discuss both theoretical results and applications in multi-scan registration, pose estimation, and scene understanding.