Name
Abstract | ||
|---|---|---|
Though recent advances in point cloud completion have shown exciting promise with learning-based methods, most of them still generate coarse point clouds with a fixed number of points (e.g. 2048). In this paper, we propose Vaccine-Style-Net, a new point cloud completion method that can produce high resolution 3D shapes with complete smooth surface. Vaccine-Style-Net performs point cloud completion in the function space of 3D surface, which represent the 3D surface as the continuous decision boundary function. Meanwhile, a reinforcement learning agent is embedded to deduce the complete 3D geometry from the incomplete point cloud. In contrast to the existing approaches, the completed 3D shapes produced by our method can be any resolution without excessive memory footprint. Moreover, to increase the diversity and adaptability of the method, we introduce two-type-free-form masks to simulate various corrupted inputs as well as a mask dataset called onion-peeling-mask (OPM). Finally, we discuss the limitations of existing evaluation metrics for shape completion tasks and explore a novel metric to supplement the existing ones. Experiments demonstrate that our method not only achieves competitive results qualitatively and quantitatively but also can produce a continuous 3D shape with any resolution.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3394171.3413648 | MM '20: The 28th ACM International Conference on Multimedia
Seattle
WA
USA
October, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7988-5 | 0 |
PageRank | References | Authors |
0.34 | 17 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Yan | 1 | 0 | 0.34 |
| Ruonan Zhang | 2 | 39 | 7.03 |
| Jing Wang | 3 | 1 | 1.74 |
| shan liu | 4 | 96 | 49.62 |
| Thomas H. Li | 5 | 73 | 13.20 |
| Ge Li | 6 | 112 | 29.37 |