According to the intelligent non-convex
asteroid shape inversion method based on the optical variation curve and constrained by
physical information, physical
prior information is fused through a
deep learning network (PINAS-Net), and rapid and accurate non-convex
asteroid three-dimensional shape inversion is achieved. Specifically, light variation curve data and a scattering parameter c serve as input, features of all curves are extracted through a light variation curve
feature extraction module (LCFEM), interaction features among multiple curves are captured through a Transform
encoder, and 1024-dimensional global features are obtained; meanwhile, expanding a scalar scattering parameter c into 16-dimensional features, fusing the 16-dimensional features with global features, and importing the 16-dimensional features into the network as physical constraints; and rough point prediction is carried out based on the fused features, and a fine
point cloud shape is obtained through a Transform decoder and three-layer up-sampling. The method provided by the invention is verified on
observation data and
simulation data of a plurality of asteroids (433 Eros, 9 Metis and 21 Luteria), and a reconstruction result is highly consistent with a
reference model. Assessment indexes show that the intersection-to-union ratio (IoU) of the method for the non-convex region can reach 0.81, the
Chamfer distance is 0.046, and compared with a traditional KTM method, the inversion speed and precision are remarkably improved. The method provides a new technical means for
asteroid shape inversion and physical parameter
estimation, and has an important
planet defense value.