Abstract—Thick distributions whose mass centers are hanging in the air is one reason lowering the accuracy of Normal Distributions Transform (NDT). This paper proposes a method to solve this problem. The proposed method is transforming reference point cloud to disc-like distributions fitting the structure of point cloud. The subdivision of cells is based on octree, and it subdivides point cloud by different sizes of cells. By Principal Component Analysis (PCA), it finds fitting plane for the partial reference point cloud in each cell. It compares the Mean Square Error (MSE) to the threshold value  to evaluate the flatness of the partial point cloud. If the cloud is not flat, the corresponding cell is subdivided to smaller cells, and those cells subdivide the cloud once more. The proposed method recursively subdivides reference point cloud until no more partial point cloud is possible to be subdivided. The experiment evaluates the loss of data and the error related to  . In addition, the flat distributions transformed from reference point cloud is shown.