PointpartNet: 3D point-cloud registration via deep part-based feature extraction

Yan, Shixun; Pathak, Sarthak; Umeda, Kazunori

doi:10.1080/01691864.2022.2084346

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…In practice, point clouds vary in size due to capture distance, sensor type, environment, and many other factors, making it difficult for traditional methods to cope with this situation. To this end, Yan et al ( 2022 ) proposed a partial feature extraction network PointpartNet. It is a neural network that divides the point cloud by KNN, extracts the features of each part, and calculates the matching likelihood score of each part.…”

Section: Incomplete Point Cloud Registrationmentioning

confidence: 99%

A review of rigid point cloud registration based on deep learning

Chen,

Feng,

et al. 2024

Front. Neurorobot.

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With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registration in actual scenes. With the above deficiency, point cloud registration methods based on deep learning technology gradually emerged. This review summarizes the point cloud registration technology based on deep learning. Firstly, point cloud registration based on deep learning can be categorized into two types: complete overlap point cloud registration and partially overlapping point cloud registration. And the characteristics of the two kinds of methods are classified and summarized in detail. The characteristics of the partially overlapping point cloud registration method are introduced and compared with the completely overlapping method to provide further research insight. Secondly, the review delves into network performance improvement summarizes how to accelerate the point cloud registration method of deep learning from the hardware and software. Then, this review discusses point cloud registration applications in various domains. Finally, this review summarizes and outlooks the current challenges and future research directions of deep learning-based point cloud registration.

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Section: Incomplete Point Cloud Registrationmentioning

confidence: 99%

A review of rigid point cloud registration based on deep learning

Chen,

Feng,

et al. 2024

Front. Neurorobot.

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“…Deep learning-based point cloud registration algorithms [16] can be divided into two categories: those based on point cloud feature extraction [17] and those that are end-to-end deep learning [18] methods. The deep learning registration method based on point cloud feature extraction is a registration method that combines deep learning with optimization methods.…”

Section: Related Workmentioning

confidence: 99%

Fast and High Accuracy 3D Point Cloud Registration for Automatic Reconstruction From Laser Scanning Data

Rao

Fan

et al. 2023

IEEE Access

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Point cloud registration from laser scanning data is a technique to establish the mapping relationship between source and target point clouds, which has been widely used in automatic 3D reconstruction, pose estimation, localization, and navigation. While algorithms like Super4PCS and MSSF-4PCS can achieve registration without initial poses, they are relatively slow, less accurate, and require iterations. To address these issues, we propose a 3D point cloud registration algorithm based on interval segmentation and multi-dimensional feature. Firstly, the source and target point clouds are segmented internally and the point cloud curvature is designed to narrow down the search range for the registration between the segmented point clouds. Secondly, the corresponding four-point sets in the segmented areas of the source and target point clouds are determined using affine invariance constraints. Finally, a multidimensional feature vector based on curvature features and fast point feature histogram is established to determine the unique corresponding four-point set pairs, and the rigid body transformation matrix is solved accordingly. Our algorithm is tested on publicly available 3D point cloud data models Bunny, Dino, Dragon, and Horse from Stanford University. Results showed that our algorithm improved registration accuracy by 24.39% and registration efficiency by 46.21% compared to the MSSF-4PCS point cloud registration algorithm. Multiple sets of experimental results confirmed the effectiveness of our algorithm. The proposed 3D point cloud registration is proved to be fast with high accuracy, which can be utilized for automatic segmentation, reconstruction, and modelling from Laser Scanning Data.

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Deep learning-based point cloud registration: a comprehensive investigation

Cheng,

Liu,

et al. 2024

International Journal of Remote Sensing

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PointpartNet: 3D point-cloud registration via deep part-based feature extraction

Cited by 5 publications

References 16 publications

A review of rigid point cloud registration based on deep learning

A review of rigid point cloud registration based on deep learning

Fast and High Accuracy 3D Point Cloud Registration for Automatic Reconstruction From Laser Scanning Data

Deep learning-based point cloud registration: a comprehensive investigation

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