The data collected by synthetic aperture radar(SAR) are sparse and incomplete because of the imaging method and geometry angle

which result in difficulties in 3D SAR data surface reconstruction. For downward-looking SAR data

this paper presents a reconstruction method that can repair sparse and incomplete data automatically. First

the Otsu method is introduced for the preprocessing of 3D SAR data. Second

the Chan-Vese model of 2D image segmentation method is applied to the surface reconstruction of the downward-looking SAR data. The distance and inner product functions are employed as the initial surface and contour indicator function. Compared with the isosurface extraction method

the proposed method can repair the holes automatically during the reconstruction procedure. The reconstructed model surface is smoother and can reflect the characteristics of the original object. The proposed method can be applied to the modeling of sparse and incomplete data.