3-D particle surface reconstruction from multiview 2-D images with structure from motion and shape from shading (January 2020)

Since wear particles usually contain valuable information about lubricant and wear condition of lubricated components, wear debris analysis is widely used to monitor machine/component wear. Current literature reports about the importance and necessity of 3-D surface characterization of wear particles as opposed to 2-D characterization. It may be borne in mind that traditional 3-D analysis has been a synonym only for local surface morphologies for stationary particles. This results in large errors when irregular particles are under observation. A new 3-D imaging methodology is proposed to reconstruct multi-view 3-D surfaces for a moving particle whereby the target particle is passed through a microchannel with rolling motion, and recorded via a digital microscope. The multiple moving particles are tracked based on Kalman filter to obtain different-view images. Sparse and dense reconstruction of the particle surfaces are enabled by these images using Structure from Motion (SfM) and Shape from Shading (SfS) methodologies, respectively. The method was verified using real particles and evaluated by the results of laser scanning confocal microscopy. The proposed method is able to reconstruct wear particles under various perspectives, and the errors from the characterization parameters of the areal are less than 20%.