Name
Title | ||
|---|---|---|
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis |
Abstract | ||
|---|---|---|
3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it becomes increasingly crucial to have a powerful 3D generative model for 3D shape synthesis and analysis. This paper proposes a deep 3D energy-based model to represent volumetric shapes. The maximum likelihood training of the... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TPAMI.2020.3045010 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Three-dimensional displays,Solid modeling,Shape,Data models,Training,Analytical models,Feature extraction | Journal | 44 |
Issue | ISSN | Citations |
5 | 0162-8828 | 1 |
PageRank | References | Authors |
0.37 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianwen Xie | 1 | 133 | 16.99 |
| Zilong Zheng | 2 | 8 | 4.87 |
| Ruiqi Gao | 3 | 21 | 9.35 |
| Wenguan Wang | 4 | 1019 | 37.24 |
| Song-Chun Zhu | 5 | 6580 | 741.75 |
| Ying Nian Wu | 6 | 1652 | 267.72 |