A Revisit of Shape Editing Techniques: from the Geometric to the Neural Viewpoint

TL;DR

This paper reviews the evolution of 3D shape editing techniques from traditional geometric methods to modern neural network approaches, highlighting recent advances and categorizing methods for organic and man-made shapes.

Contribution

It provides a comprehensive survey of shape editing techniques, emphasizing the transition from geometric to neural methods and categorizing recent research.

Findings

Traditional methods rely on optimal transformations and weights.

Data-driven approaches improve editing robustness and speed.

Neural deformation techniques are increasingly popular.

Abstract

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 term. 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 is naturally data-driven. We mainly survey recent research works 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.