Neural Shape

Neural shape research focuses on representing and manipulating 3D shapes using neural networks, aiming to overcome limitations of traditional methods in handling complex topologies and achieving intuitive shape editing. Current efforts concentrate on developing novel architectures like neural generalized cylinders and neural distance fields, employing techniques such as coupled neural shape optimization and isometric mapping in latent spaces to enable efficient and controllable shape transformations. This field is significant for its potential to advance various applications, including computer graphics, robotics (e.g., object assembly), and solving differential equations through improved shape representation and manipulation capabilities.