Name
Title | ||
|---|---|---|
Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views. |
Abstract | ||
|---|---|---|
Deep learning has achieved remarkable results in 3D shape analysis by learning global shape features from the pixel-level over multiple views. Previous methods, however, compute low-level features for entire views without considering part-level information. In contrast, we propose a deep neural network, called Parts4Feature, to learn 3D global features from part-level information in multiple views. We introduce a novel definition of generally semantic parts, which Parts4Feature learns to detect in multiple views from different 3D shape segmentation benchmarks. A key idea of our architecture is that it transfers the ability to detect semantically meaningful parts in multiple views to learn 3D global features. Parts4Feature achieves this by combining a local part detection branch and a global feature learning branch with a shared region proposal module. The global feature learning branch aggregates the detected parts in terms of learned part patterns with a novel multi-attention mechanism, while the region proposal module enables locally and globally discriminative information to be promoted by each other. We demonstrate that Parts4Feature outperforms the state-of-the-art under three large-scale 3D shape benchmarks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.24963/ijcai.2019/108 | IJCAI |
Field | DocType | Volume |
Computer science,Artificial intelligence,Natural language processing,Machine learning | Journal | abs/1905.07506 |
Citations | PageRank | References |
6 | 0.41 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Zhizhong | 1 | 198 | 18.28 |
| Xinhai Liu | 2 | 32 | 2.17 |
| Yu-shen Liu | 3 | 319 | 23.20 |
| Zwicker Matthias | 4 | 2513 | 129.25 |