In order to study brain functions and connectome, scientists need to register and warp source image data of some model organism, e.g. Drosophila, into a pre-defined standard atlas. Such registration and warping procedure is conventionally driven and constrained by point clouds extracted from contour edges of manually-segmented landmark tissues within a 2D+Z image volume. However, it is difficult to register two dense 3D point clouds. The fitness between spatial distributions of two point clouds cannot guarantee the matchness between 3D anatomical surfaces from which point clouds were sampled. Hence, to settle down this problem, we propose in this paper a strategy to register 3D point clouds of Drosophila brain in 2D parameterization domain. Our contributions are twofold. First, instead of registering point clouds directly, our method was designed to register two mesh surface models, each defined by a to-be-registered point clouds, so that the anatomical shape details described by the point cloud can be aligned after registration. Second, the proposed method performs registration/warping in a parameterization domain, and hence it no longer needs a rigid transformation to globally align and scale the input models. Experiments show that the surface-to-surface distance is reduced after registration and warping process. For models with an about 1100-voxel-long bounding box diagonal, the average surface-to-surface distance is reduced to about 0.1 voxel after registration. The proposed method is effective.