Laga, HamidTabia, HediBærentzen, Jakob Andreas and Hildebrandt, Klaus2017-07-022017-07-0220171467-8659https://doi.org/10.1111/cgf.13241https://diglib.eg.org:443/handle/10.1111/cgf13241We introduce co-variation analysis as a tool for modeling the way part geometries and configurations co-vary across a family of man-made 3D shapes. While man-made 3D objects exhibit large geometric and structural variations, the geometry, structure, and configuration of their individual components usually do not vary independently from each other but in a correlated fashion. The size of the body of an airplane, for example, constrains the range of deformations its wings can undergo to ensure that the entire object remains a functionally-valid airplane. These co-variation constraints, which are often non-linear, can be either physical, and thus they can be explicitly enumerated, or implicit to the design and style of the shape family. In this article, we propose a data-driven approach, which takes pre-segmented 3D shapes with known component-wise correspondences and learns how various geometric and structural properties of their components co-vary across the set. We demonstrate, using a variety of 3D shape families, the utility of the proposed co-variation analysis in various applications including 3D shape repositories exploration and shape editing where the propagation of deformations is guided by the co-variation analysis. We also show that the framework can be used for context-guided orientation of objects in 3D scenes.I.3.3 [Computer Graphics]Computational Geometry and Object ModelingGeometric algorithms10.1111/cgf.13241013-025