In this thesis, we proposed a novel approach for reconstructing high-resolution 3D human body from extremely small number of 3D points which represent the parts of a human body. We leverage a data set of high-resolution 3D models of 100 humans varying from each other by physical attributes such as age, weight, size etc. We, first, divide the bodies in database into seven semantic regions (namely; head, left arm, right arm, chest, belly, left leg, right leg). Then, for each input region consisting of maximum 40 points, we search the database for the best matching shape. For the matching criteria, we use the distance between novel point-base features of input points and body parts in the database. We further combine the matched parts from different bodies into one body which result in a high resolution human body, with the help of Laplacian deformation. To evaluate our results, we pick points from each part of the ground truth bodies, then reconstruct them using our method and compare the resulting bodies with corresponding ground truth bodies. Also, our results are compared with ARAP-based results. In addition, we run our algorithm with noisy data, which produces human bodies which do not exist in our database. Our experiments show that the proposed approach reconstructs human bodies with different physical attributes from small number of points successfully.