Abstract
3D shape generation is a challenging problem due to the high-dimensional output space and complex part configurations
of real-world objects. As a result, existing algorithms experience difficulties in accurate generative modeling of 3D shapes. Here, we propose a novel factorized generative model for 3D shape generation that sequentially transitions from coarse to fine scale shape generation. To this end, we introduce an unsupervised primitive discovery algorithm based
on a higher-order conditional random field model. Using the primitive parts for shapes as attributes, a parameterized
3D representation is modeled in the first stage. This representation is further refined in the next stage by adding fine
scale details to shape. Our results demonstrate improved representation ability of the generative model and better
quality samples of newly generated 3D shapes. Further, our primitive generation approach can accurately parse common
objects into a simplified representation.
of real-world objects. As a result, existing algorithms experience difficulties in accurate generative modeling of 3D shapes. Here, we propose a novel factorized generative model for 3D shape generation that sequentially transitions from coarse to fine scale shape generation. To this end, we introduce an unsupervised primitive discovery algorithm based
on a higher-order conditional random field model. Using the primitive parts for shapes as attributes, a parameterized
3D representation is modeled in the first stage. This representation is further refined in the next stage by adding fine
scale details to shape. Our results demonstrate improved representation ability of the generative model and better
quality samples of newly generated 3D shapes. Further, our primitive generation approach can accurately parse common
objects into a simplified representation.
| Original language | English |
|---|---|
| Title of host publication | IEEE Conference on Computer Vision and Pattern Recognition |
| Publisher | IEEE, Institute of Electrical and Electronics Engineers |
| Pages | 1-10 |
| Number of pages | 10 |
| Publication status | In preparation - 2019 |
| Event | IEEE Conference on Computer Vision and Pattern Recognition - Long Beach, United States Duration: 15 Jun 2019 → 20 Jun 2019 http://cvpr2019.thecvf.com/ |
Conference
| Conference | IEEE Conference on Computer Vision and Pattern Recognition |
|---|---|
| Abbreviated title | CVPR 2019 |
| Country/Territory | United States |
| City | Long Beach |
| Period | 15/06/19 → 20/06/19 |
| Internet address |