Mr. Hongyan An Profile

Hongyan An

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Proceedings Article | 26 June 2023 Paper

Deep closest point registration based on hybrid features

Hongyan An, Zhuo Li

Proceedings Volume 12721, 127210O (2023) https://doi.org/10.1117/12.2683401

KEYWORDS: Point clouds, Data modeling, Education and training, Feature extraction, Singular value decomposition, Matrices, Error analysis, Evolutionary algorithms, 3D modeling, Semantics

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The core problem of point cloud registration can be explained as computing rigid transformations to align two point clouds, which is a key technology used in popular fields such as three-dimensional reconstruction, robotics, and autonomous driving. We come up with a hybrid feature-based model inspired by Deep Closest Point (DCP) and Robust Point Matching using Learned Features (RPMNET). The main innovation of this model is to combine abstract features extracted by Dynamic Graph CNN (DGCNN) with Point Pair Features (PPF) as hybrid features for point cloud registration, after that, soft matching is performed between two point clouds, and then singular value decomposition (SVD) is applied to compute the rigid transformations. Besides, we adopt the ModelNet40 dataset for training and compare the trained model with DCPV2, Iterative Closest Point (ICP) and some other ICP variants, the comparison of results indicates our model performs better than the above methods in predicting the angle of rotation when rigid transformations occur. We also test our model on clean and noise-added test sets respectively to verify the robustness of it.

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