3D surface reconstruction of apples from 2D NIR images

Machine vision methods are widely used in apple defect detection and quality grading applications. Currently, 2D near-infrared (NIR) imaging of apples is often used to detect apple defects because the image intensity of defects is different from normal apple parts. However, a drawback of this method is that the apple calyx also exhibits similar image intensity to the apple defects. Since an apple calyx often appears in the NIR image, the false alarm rate is high with the 2D NIR imaging method. In this paper, a 2D NIR imaging method is extended to a 3D reconstruction so that the apple calyx can be differentiated from apple defects according to their different 3D depth information. The Lambertian model is used to evaluate the reflectance map of the apple surface, and then Pentland's Shape-From-Shading (SFS) method is applied to reconstruct the 3D surface information of the apple based on Fast Fourier Transform (FFT). Pentland's method is directly derived from human perception properties, making it close to the way human eyes recover 3D information from a 2D scene. In addition, the FFT reduces the computation time significantly. The reconstructed 3D apple surface maps are shown in the results, and different depths of apple calyx and defects are obtained correctly.