Deep closest point registration based on hybrid features

Hongyan An; Zhuo Li

doi:10.1117/12.2683401

26 June 2023 Deep closest point registration based on hybrid features

Hongyan An, Zhuo Li

Proceedings Volume 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023); 127210O (2023) https://doi.org/10.1117/12.2683401
Event: 2023 2nd International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2023, Chengdu, China

Abstract

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.

Citation Download Citation

Hongyan An and Zhuo Li "Deep closest point registration based on hybrid features", Proc. SPIE 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 127210O (26 June 2023); https://doi.org/10.1117/12.2683401

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KEYWORDS

Point clouds

Data modeling

Education and training

Feature extraction

Singular value decomposition

Error analysis

Matrices

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