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- Title
PointCNT: A One-Stage Point Cloud Registration Approach Based on Complex Network Theory.
- Authors
Wu, Xin; Wei, Xiaolong; Xu, Haojun; Li, Caizhi; Hou, Yuanhan; Yin, Yizhen; He, Weifeng
- Abstract
Inspired by the parallel visual pathway model of the human neural system, we propose an efficient and high-precision point cloud registration method based on complex network theory (PointCNT). A deep learning network (DNN) design method based on complex network theory is proposed, and a multipath feature extraction network, namely, Complex Kernel Point Convolution Neural Network (ComKP-CNN) for point clouds is designed based on the design method. Self-supervision is introduced to improve the feature extraction ability of the model. A feature embedding module is proposed to explicitly embed the transformation-variant coordinate information and transformation-invariant distance information into features. A feature fusion module is proposed to enable the source and template point clouds to perceive each other's nonlocal features. Finally, a Multilayer Perceptron (MLP) with prominent fitting characteristics is utilized to estimate the transformation matrix. The experimental results show that the Registration Recall (RR) of PointCNT on ModelNet40 dataset reached 96.4%, significantly surpassing one-stage methods such as Feature-Metric Registration (FMR) and approaching two-stage methods such as Geometric Transformer (GeoTransformer). The computation speed is faster than two-stage methods, and the registration run time is 0.15 s. In addition, ComKP-CNN is universal and can improve the registration accuracy of other point cloud registration methods.
- Subjects
POINT cloud; DEEP learning; CONVOLUTIONAL neural networks; FEATURE extraction; VISUAL pathways; RECORDING &; registration
- Publication
Remote Sensing, 2023, Vol 15, Issue 14, p3545
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs15143545