| Full name | MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation |
| Description | We propose a novel framework to group and refine the 3D instances. In practice, we first learn an offset vector for each point and shift it to its predicted instance center. To better group these points, we propose a Hierarchical Point Grouping algorithm to merge the centrally aggregated points progressively. All points are grouped into small clusters, which further gradually undergo another clustering procedure to merge into larger groups. These multi-scale groups are exploited for instance prediction, which is beneficial for predicting instances with different scales. In addition, a novel MaskScoreNet is developed to produce binary point masks of these groups for further refining the segmentation results. |
| Publication title | MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation |
| Publication authors | Min Zhong, Xinghao Chen, Xiaokang Chen, Gang Zeng, Yunhe Wang |
| Publication venue | ICME 2022 |
| Publication URL | https://arxiv.org/abs/2203.14662 |
| Input Data Types | Uses Color,Uses Geometry Uses 3D |
| Programming language(s) | python |
| Hardware | 1080ti |
| Submission creation date | 7 Nov, 2020 |
| Last edited | 29 Mar, 2022 |