| Full name | Semantic Superpoint Tree Network |
| Description | Instance segmentation in 3D scenes is fundamental in many applications of scene understanding. It is yet challenging due to the compound factors of data irregularity and uncertainty in the numbers of instances. State-of-the-art methods largely rely on a general pipeline that first learns point-wise features discriminative at semantic and instance levels, followed by a separate step of point grouping for proposing object instances. While promising, they have the shortcomings that (1) the second step is not supervised by the main objective of instance segmentation, and (2) their point-wise feature learning and grouping are less effective to deal with data irregularities, possibly resulting in fragmented segmentations. To address these issues, we propose in this work an end-to-end solution of Semantic Superpoint Tree Network (SSTNet) for proposing object instances from scene points. Key in SSTNet is an intermediate, semantic superpoint tree (SST), which is constructed based on the learned |
| Publication title | Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks |
| Publication authors | Zhihao Liang, Zhihao Li, Songcen Xu, Mingkui Tan, Kui Jia |
| Publication venue | ICCV2021 |
| Publication URL | https://arxiv.org/pdf/2108.07478.pdf |
| Input Data Types | Uses Color,Uses Geometry Uses 3D |
| Programming language(s) | Python with CUDA |
| Hardware | RTX 3090 |
| Website | https://github.com/Gorilla-Lab-SCUT/SSTNet |
| Source code or download URL | https://github.com/Gorilla-Lab-SCUT/SSTNet |
| Submission creation date | 4 Jan, 2021 |
| Last edited | 19 Aug, 2021 |
