Learning Gradient Fields for Shape Generation

• URL: http://arxiv.org/abs/2008.06520v2
• Date: Tue, 18 Aug 2020 04:34:18 GMT
• Title: Learning Gradient Fields for Shape Generation
• Authors: Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, and Bharath Hariharan
• Abstract summary: A point cloud can be viewed as samples from a distribution of 3D points whose density is concentrated near the surface of the shape. We generate point clouds by performing gradient ascent on an unnormalized probability density. 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.
• Score: 69.85355757242075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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