Point cloud source data for surface reconstruction is usually contaminated with noise and outliers. To overcome this deficiency, a density-based point cloud denoising method is presented to remove outliers and noisy points. First, particle-swam optimization technique is employed for automatically approximating optimal bandwidth of multivariate kernel density estimation to ensure the robust performance of density estimation. Then, mean-shift based clustering technique is used to remove outliers through a thresholding scheme. After removing outliers from the point cloud, bilateral mesh filtering is applied to smooth the remaining points. The experimental results show that this approach, comparably, is robust and efficient.

In this paper, we propose four different geometric measures to identify appropriate triangles to be simplified in 3D complex model. Each measure yields different weight on the same surface and produces a unique simplified model that worth to be analyzed. The proposed measures involve consideration on the resulting of the surfaces collapse, the high peak and low peak of the triangles mesh, the irregular triangle shape, the capacity and boundary view on the triangles mesh. The chosen triangle is to be collapsed based criterion on Half-edge Collapse Transformation method. From the empirical results, one of the proposed measures presents almost excellence in all the criteria mentioned above. The empirical results include the quality of the surface models (visualization purpose), the efficiency of the measures and the overall appearance preservation of the simplified models. The proposed measures are then to be compared to three existing measures. From the analyzed results, we combine the measures to adapt to the user's response for generating the user-desired simplified models.

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