Learning Gradient Fields for Shape Generation
This addresses shape generation for computer graphics and 3D modeling, with incremental improvements in method and performance.
The paper tackles the problem of generating 3D shapes from point cloud data by moving randomly sampled points to high-density areas, achieving state-of-the-art performance in point cloud auto-encoding and generation while enabling high-quality implicit surface extraction.
In this work, we propose a novel technique to generate shapes from point cloud data. A point cloud can be viewed as samples from a distribution of 3D points whose density is concentrated near the surface of the shape. Point cloud generation thus amounts to moving randomly sampled points to high-density areas. We generate point clouds by performing stochastic gradient ascent on an unnormalized probability density, thereby moving sampled points toward the high-likelihood regions. Our model directly predicts the gradient of the log density field and can be trained with a simple objective adapted from score-based generative models. We show that our method can reach state-of-the-art performance for point cloud auto-encoding and generation, while also allowing for extraction of a high-quality implicit surface. Code is available at https://github.com/RuojinCai/ShapeGF.