论文标题
DeepFit：通过神经网络加权最小二乘的3D表面拟合
DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares
论文作者
论文摘要
我们为非结构化3D点云提出了一种表面拟合方法。该方法称为DeepFit，结合了一个神经网络，以学习加权最小二乘多项式表面拟合的点重量。学习的权重作为表面点附近的软选择，从而避免了先前方法所需的比例选择。为了训练网络，我们提出了一种新型的表面一致性损失，以改善点重量估计。该方法使提取正常的向量和其他几何特性（例如主曲线），后者在训练过程中没有作为地面真相提出。我们在基准的正常和曲率估计数据集上实现最新的结果，证明了噪声，离群值和密度变化的稳健性，并显示了其在去除噪声中的应用。
We propose a surface fitting method for unstructured 3D point clouds. This method, called DeepFit, incorporates a neural network to learn point-wise weights for weighted least squares polynomial surface fitting. The learned weights act as a soft selection for the neighborhood of surface points thus avoiding the scale selection required of previous methods. To train the network we propose a novel surface consistency loss that improves point weight estimation. The method enables extracting normal vectors and other geometrical properties, such as principal curvatures, the latter were not presented as ground truth during training. We achieve state-of-the-art results on a benchmark normal and curvature estimation dataset, demonstrate robustness to noise, outliers and density variations, and show its application on noise removal.
