3D shape editing is widely used in a range of applications such as movie production, computer games and computer aided design. It is also a popular research topic in computer graphics and computer vision. In past decades, researchers have developed a series of editing methods to make the editing process faster, more robust, and more reliable. Traditionally, the deformed shape is determined by the optimal transformation and weights for an energy formulation. With increasing availability of 3D shapes on the Internet, data-driven methods were proposed to improve the editing results. More recently as the deep neural networks became popular, many deep learning based editing methods have been developed in this field, which are naturally data-driven. We mainly survey recent research studies from the geometric viewpoint to those emerging neural deformation techniques and categorize them into organic shape editing methods and man-made model editing methods. Both traditional methods and recent neural network based methods are reviewed.

3D 形状编辑广泛应用于电影制作、电脑游戏和电脑辅助设计等一系列应用中。它也是计算机图形学和计算机视觉领域的热门研究课题。在过去的几十年里，研究人员开发了一系列编辑方法，使编辑过程更快、更健壮、更可靠。传统上，变形形状由能量公式的最佳变换和权重决定。随着 Internet 上 3D 形状可用性的增加，提出了数据驱动的方法来改善编辑结果。最近随着深度神经网络的流行，该领域已经开发了许多基于深度学习的编辑方法，这些方法自然是数据驱动的。我们主要从几何角度对最近出现的神经变形技术进行研究，并将其分为有机形状编辑方法和人造模型编辑方法。回顾了传统方法和最近的基于神经网络的方法。