Name
Abstract | ||
|---|---|---|
In this paper, we present a hybrid semantic affinity learning method (HSA) to capture and leverage the dependencies of categories for 3D semantic segmentation. Unlike existing methods that only use the cross-entropy loss to perform one-to-one supervision and ignore the semantic relations between points, our approach aims to learn the label dependencies between 3D points from a hybrid perspective. From a global view, we introduce the structural correlations among different classes to provide global priors for point features. Specifically, we fuse word embeddings of labels and scene-level features as category nodes, which are processed via a graph convolutional network (GCN) to produce the sample-adapted global priors. These priors are then combined with point features to enhance the rationality of semantic predictions. From a local view, we propose the concept of local affinity to effectively model the intra-class and inter-class semantic similarities for adjacent neighborhoods, making the predictions more discriminative. Experimental results show that our method consistently improves the performance of state-of-the-art models across indoor (S3DIS, ScanNet), outdoor (SemanticKITTI), and synthetic (ShapeNet) datasets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TCSVT.2021.3132047 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | DocType | Volume |
Semantic segmentation,point cloud,affinity,semantic priors,category dependencies,graph convolutional network | Journal | 32 |
Issue | ISSN | Citations |
7 | 1051-8215 | 0 |
PageRank | References | Authors |
0.34 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhanjie Song | 1 | 11 | 3.93 |
| Linqing Zhao | 2 | 0 | 0.34 |
| Jie Zhou | 3 | 2103 | 190.17 |