Learning Self-prior for Mesh Denoising Using Dual Graph Convolutional Networks
Learning Self-prior for Mesh Denoising Using Dual Graph Convolutional Networks
复制标题
DOI：
10.1007/978-3-031-20062-5_21
复制
发表时间：
2022
影响因子：
2.3
通讯作者：
Shota Hattori;Tatsuya Yatagawa;Y. Ohtake;H. Suzuki
中科院分区：
文献类型：
--
作者：
Shota Hattori;Tatsuya Yatagawa;Y. Ohtake;H. Suzuki
本研究提出了一个深度学习框架，用于从单个噪声输入进行网格去噪，其中两个图卷积网络被联合训练以分离过滤顶点位置和面法线。仅从单个输入获得的先验被特别称为自先验。该方法利用深度图像先验（DIP）框架，利用卷积神经网络（CNN）获得图像恢复的自先验。因此，我们得到了一个去噪的网格，没有任何无地真噪声网格。与原始DIP通过神经网络将固定随机码转换为无噪声图像相比，我们从固定随机码中再现顶点位移，并从总结局部三角形排列的特征向量中再现facet法线。在使用几个验证样本调整了几个超参数之后，我们的方法比使用单个噪声输入网格的传统方法取得了显着更高的性能。此外，该方法的性能优于其他使用大规模形状数据集训练的深度神经网络的方法。无论是大规模数据集还是地面真实无噪声网格，我们的方法都具有独立性，这将使我们能够轻松地对形状数据集中很少包含的网格进行去噪。我们的代码可在：https://github.com/astaka-pe/Dual-DMP.git。
This study proposes a deep-learning framework for mesh denoising from a single noisy input, where two graph convolutional networks are trained jointly to filter vertex positions and facet normals apart. The prior obtained only from a single input is particularly referred to as a self-prior. The proposed method leverages the framework of the deep image prior (DIP), which obtains the self-prior for image restoration using a convolutional neural network (CNN). Thus, we obtain a denoised mesh without any ground-truth noise-free meshes. Compared to the original DIP that transforms a fixed random code into a noise-free image by the neural network, we reproduce vertex displacement from a fixed random code and reproduce facet normals from feature vectors that summarize local triangle arrangements. After tuning several hyperparameters with a few validation samples, our method achieved significantly higher performance than traditional approaches working with a single noisy input mesh. Moreover, its performance is better than the other methods using deep neural networks trained with a large-scale shape dataset. The independence of our method of either large-scale datasets or ground-truth noise-free mesh will allow us to easily denoise meshes whose shapes are rarely included in the shape datasets. Our code is available at: https://github.com/astaka-pe/Dual-DMP.git.