DeepFit：基于神经网络加权最小二乘的三维曲面拟合
DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares
摘要：
提出了一种非结构化三维点云的曲面拟合方法。这种方法被称为DeepFit，它结合了一个神经网络来学习加权最小二乘多项式曲面拟合的逐点权重。学习的权重作为表面点邻域的软选择，从而避免了以前方法所需的尺度选择。为了训练网络，我们提出了一种新的表面一致性损失，提高了点的权重估计。该方法能够提取法向量和其他几何属性，例如主曲率，后者在训练期间不作为地面实况呈现。我们在基准正常和曲率估计数据集上实现了最先进的结果，证明了对噪声，离群值和密度变化的鲁棒性，并展示了其在噪声去除上的应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
