Learning Gradient Fields for Shape Generation
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Pages (from-to) | 364-381 |
Number of pages | 18 |
ISSN | 0302-9743 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | 16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom Duration: 23 Aug 2020 → 28 Aug 2020 |
Conference
Conference | 16th European Conference on Computer Vision, ECCV 2020 |
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Country | United Kingdom |
City | Glasgow |
Period | 23/08/2020 → 28/08/2020 |
Bibliographical note
Funding Information:
Acknowledgment. This work was supported in part by grants from Magic Leap and Facebook AI, and the Zuckerman STEM leadership program.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
- 3D generation, Generative models
Research areas
ID: 301818367