Department of Computer Science and Engineering, Interdisciplinary Graduate Program in Medical Bigdata Convergence, Kangwon National University, Chuncheon 24341, Korea.
Sensors (Basel). 2022 Aug 27;22(17):6457. doi: 10.3390/s22176457.
Three-dimensional mesh post-processing is an important task because low-precision hardware and a poor capture environment will inevitably lead to unordered point clouds with unwanted noise and holes that should be suitably corrected while preserving the original shapes and details. Although many 3D mesh data-processing approaches have been proposed over several decades, the resulting 3D mesh often has artifacts that must be removed and loses important original details that should otherwise be maintained. To address these issues, we propose a novel 3D mesh completion and denoising system with a deep learning framework that reconstructs a high-quality mesh structure from input mesh data with several holes and various types of noise. We build upon SpiralNet by using a variational deep autoencoder with anisotropic filters that apply different convolutional filters to each vertex of the 3D mesh. Experimental results show that the proposed method enhances the reconstruction quality and achieves better accuracy compared to previous neural network systems.
三维网格后处理是一项重要的任务，因为低精度的硬件和较差的捕获环境将不可避免地导致无序的点云，其中包含不需要的噪声和空洞，这些应该在保留原始形状和细节的同时进行适当的纠正。虽然几十年来已经提出了许多 3D 网格数据处理方法，但得到的 3D 网格通常存在必须去除的伪影，并且会丢失原本应该保留的重要原始细节。为了解决这些问题，我们提出了一种新颖的基于深度学习框架的 3D 网格补全和去噪系统，该系统可以从具有多个孔和各种类型噪声的输入网格数据中重建高质量的网格结构。我们通过使用具有各向异性滤波器的变分深度自动编码器在 SpiralNet 上进行构建，该滤波器将不同的卷积滤波器应用于 3D 网格的每个顶点。实验结果表明，与以前的神经网络系统相比，所提出的方法提高了重建质量并实现了更好的准确性。
