We report the 3D-tracking of irregular sand particles in a wave flume using a cylindrical interferometric particle imaging set-up. The longitudinal position of each particle is deduced from the ellipticity of its speckle-like interferometric image. The size of a particle is determined from the analysis of the 2D Fourier transform of its defocused image. It is further possible to identify some rotation of the particles. Simulations accurately confirm the experimental determination of the different parameters (3D position and size of each particle).

A convolutional neural network (CNN) was used to identify the morphology of rough particles from their interferometric images. The tested particles had the shapes of sticks, crosses, and dendrites as well as Y-like, L-like, and T-like shapes. A conversion of the interferometric images to polar coordinates enabled particle shape recognition despite the random orientations and random sizes of the particles. For the non-centrosymmetric particles (Y, L, and T), the CNN was not disturbed by the twin image problem, which would affect some classical reconstructions based on phase retrieval algorithms. A 100% recognition rate was obtained.