Hierarchical Bidirected Graph Convolutions for Large-Scale 3-D Point Cloud Place Recognition
- Authors
- Shu, D.W.; Kwon, Junseok
- Issue Date
- Jan-2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Bidirected graph convolution; Data mining; Directed graphs; Feature extraction; hierarchical graph convolution; Image edge detection; Kernel; large-scale 3-D point cloud place recognition; Point cloud compression; pooling and fusing edges; Sensors
- Citation
- IEEE Transactions on Neural Networks and Learning Systems
- Journal Title
- IEEE Transactions on Neural Networks and Learning Systems
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/66351
- DOI
- 10.1109/TNNLS.2023.3236313
- ISSN
- 2162-237X
2162-2388
- Abstract
- In this article, we present a novel hierarchical bidirected graph convolution network (HiBi-GCN) for large-scale 3-D point cloud place recognition. Unlike place recognition methods based on 2-D images, those based on 3-D point cloud data are typically robust to substantial changes in real-world environments. However, these methods have difficulty in defining convolution for point cloud data to extract informative features. To solve this problem, we propose a new hierarchical kernel defined as a hierarchical graph structure through unsupervised clustering from the data. In particular, we pool hierarchical graphs from the fine to coarse direction using pooling edges and fuse the pooled graphs from the coarse to fine direction using fusing edges. The proposed method can, thus, learn representative features hierarchically and probabilistically; moreover, it can extract discriminative and informative global descriptors for place recognition. Experimental results demonstrate that the proposed hierarchical graph structure is more suitable for point clouds to represent real-world 3-D scenes. IEEE
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/66351)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.