Smooth, exact rotational symmetrization for deep learning on point clouds

Point clouds are versatile representations of 3D objects and have found widespread application in science and engineering. Many successful deep-learning models have been proposed that use them as input. The domain of chemical and materials modeling is especially challenging because exact compliance with physical constraints is highly desirable for a model to be usable in practice. These constraints include smoothness and invariance with respect to translations, rotations, and permutations of identical atoms. If these requirements are not rigorously fulfilled, atomistic …

3d objects application compliance cond-mat.mtrl-sci constraints cs.cv cs.lg deep learning domain engineering found materials modeling objects physics.chem-ph science them