Abstract
Point cloud registration is a key step in the reconstruction of 3D data models. The traditional ICP registration algorithm depends on the initial position of the point cloud. Otherwise, it may get trapped into local optima. In addition, the registration method based on the feature learning of PointNet cannot directly or effectively extract local features. To solve these two problems, this paper proposes SAP-Net, inspired by CorsNet and PointNet++, as an optimized CorsNet. To be more specific, SAP-Net firstly uses the set abstraction layer in PointNet++ as the feature extraction layer and then combines the global features with the initial template point cloud. Finally, PointNet is used as the transform prediction layer to obtain the six parameters required for point cloud registration directly, namely the rotation matrix and the translation vector. Experiments on the ModelNet40 dataset and real data show that SAP-Net not only outperforms ICP and CorsNet on both seen and unseen categories of the point cloud but also has stronger robustness.
Highlights
Network Based on Local ShapeThe 3D point cloud data have incomparable advantages over 2D images, which can accurately record the 3D shape, geometric size, space coordinates, and other information of the object surface
It can be found that the normal distribution transform (NDT) based on probability distribution uses the matrix method to solve the point cloud matching [10]
We propose an end-to-end point cloud registration network, based on deep learning, called SAP-Net
Summary
The 3D point cloud data have incomparable advantages over 2D images, which can accurately record the 3D shape, geometric size, space coordinates, and other information of the object surface. DCP [20], RPM-Net [21,22], and CorsNet [23] can be used to achieve higher accuracy for the registration results of seen and unseen categories These methods rely on inputs with unique local geometric features to predict reliable feature point matching, so they are more sensitive to noise and other interference. We propose an end-to-end point cloud registration network, based on deep learning, called SAP-Net. Inspired by CorsNet and PointNet++, SAP-Net is classified into a feature extraction layer (set abstraction (SA)) and a transform prediction layer. Unlike the fully connected and SVD methods, we used the PointNet structure as the transform prediction layer to obtain the rigid transformation directly, which reduced the complexity of the network and effectively utilized the local shape features and global features of two point clouds; We compared the proposed method with other methods and evaluated them.
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