  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.
