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Unorganized point clouds obtained from 3D shape acquisition devices usually present noise, outliers, and nonuniformities. The proposed framework consolidates unorganized points through an iterative procedure of interlaced downsampling and upsampling. Selection operations remove outliers while preserving geometric details. The framework improves the uniformity of points by moving the downsampled particles and refining point samples. Surface extrapolation fills missed regions. Moreover, an adaptive sampling strategy speeds up the iterations. Experimental results demonstrate the framework's effectiveness.