skeletal model denosing with cascaded network With the continuous breakthrough of threedimensional(3D) data visualization and 3D printing technology, there are many achievements in the combination field of computer science and medical science. Especially in orthopedics, the 3D printed skeletal model and prosthesis have a good effect on surgeonu0027s surgery and patient rehabilitation process. The 3D printed skeletal model plays a vital role in the surgical process, and the accuracy of the threedimensional printed skeletal model determines the extent of this auxiliary effect. In the field of orthopedics, we propose to use the datadriven the radial basis function(RBF) network to remove noise from the surface of the skeletal model. The RBF network achieves the goals of denoising by learning the regression relationship between the noisy mesh and the geometry descriptor called the filtered facet normal descriptor (FND) based on bilateral normal filters of the corresponding smooth facet, without making many unreasonable assumptions made by the traditional denoising algorithms. Based on the cascaded model, we adjusts the noise level on the training model according to the real situation of the bone reconstruction model, and changes the structure of the geometric descriptor composed of the threelayer cascaded regression function network. The cascaded regression model suitable for skeletal threedimensional model denoising is trained while maintaining the mesh mean normal vector deviation angle of 4.2xc2xb0. The model not only removes the noise of the bone 3D model, but also maintains the geometric characteristics of the skeleton model itself.

Fast mesh denoising with data driven normal filtering using deep autoencoders Through the years, several works have demonstrated highquality 3D mesh denoising. Despite the high reconstruction quality, there are still challenges that need to be addressed ranging from variations in configuration parameters to high computational complexity. These drawbacks are crucial especially if the reconstructed models have to be used for quality check, inspection or repair in manufacturing environments where we have to deal with large objects resulting in very dense 3D meshes. Recently, deep learning techniques have shown that are able to automatically learn and find more accurate and reliable results, without the need for setting manually parameters. In this work, motivated by the aforementioned requirements, we propose a fast and reliable denoising method that can be effectively applied for reconstructing very dense noisy 3D models. The proposed method applies conditional variational autoencoders on face normals. Extensive evaluation studies carried out using a variety of 3D models verify that the proposed approach achieves plausible reconstruction outputs, very relative or even better of those proposed by the literature, in considerably faster execution times.