Name
Abstract | ||
|---|---|---|
Instance segmentation is a problem of significance in computer vision. However, preparing annotated data for this task is extremely time-consuming and costly. By combining the advantages of 3D scanning, physical reasoning, and GAN techniques, we introduce a novel pipeline named Geometry-guided GAN (GeoGAN) to obtain large quantities of training samples with minor annotation. Our pipeline is well-suited to most indoor and some outdoor scenarios. To evaluate our performance, we build a new Instance-60K dataset, with various of common objects categories. Extensive experiments show that our pipeline can achieve decent instance segmentation performance given very low human annotation cost. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Annotation,Segmentation,Computer science,Artificial intelligence,Physical reasoning,Machine learning |
DocType | Volume | Citations |
Journal | abs/1801.08839 | 0 |
PageRank | References | Authors |
0.34 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenqiang Xu | 1 | 0 | 3.04 |
| Yonglu Li | 2 | 22 | 7.05 |
| Cewu Lu | 3 | 993 | 62.08 |