Partial point cloud registration algorithm based on deep learning and non-corresponding point estimation

Wang, Shenyi; Kang, Zhilong; Chen, Lei; Guo, Yanju; Zhao, Yuchen; Chai, Yuanfei

doi:10.1007/s00371-023-03103-6

Vis Comput

2023

DOI: 10.1007/s00371-023-03103-6

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Partial point cloud registration algorithm based on deep learning and non-corresponding point estimation

Shenyi Wang,

Zhilong Kang,

Lei Chen

et al.

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2024

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Cited by 2 publications

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“…These are standard and popular methods, often used in real-world applications [3,26,48]. Furthermore, we selected learning-based methods for their improved robustness and accuracy when dealing with extensive real-world scans [10,39,49]. Additionally, there is a need to benchmark these methods in the literature [30].…”

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confidence: 99%

Comparison of Point Cloud Registration Techniques on Scanned Physical Objects

Denayer,

De Winter,

Bernardes

et al. 2024

Sensors

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This paper presents a comparative analysis of six prominent registration techniques for solving CAD model alignment problems. Unlike the typical approach of assessing registration algorithms with synthetic datasets, our study utilizes point clouds generated from the Cranfield benchmark. Point clouds are sampled from existing CAD models and 3D scans of physical objects, introducing real-world complexities such as noise and outliers. The acquired point cloud scans, including ground-truth transformations, are made publicly available. This dataset includes several cleaned-up scans of nine 3D-printed objects. Our main contribution lies in assessing the performance of three classical (GO-ICP, RANSAC, FGR) and three learning-based (PointNetLK, RPMNet, ROPNet) methods on real-world scans, using a wide range of metrics. These include recall, accuracy and computation time. Our comparison shows a high accuracy for GO-ICP, as well as PointNetLK, RANSAC and RPMNet combined with ICP refinement. However, apart from GO-ICP, all methods show a significant number of failure cases when applied to scans containing more noise or requiring larger transformations. FGR and RANSAC are among the quickest methods, while GO-ICP takes several seconds to solve. Finally, while learning-based methods demonstrate good performance and low computation times, they have difficulties in training and generalizing. Our results can aid novice researchers in the field in selecting a suitable registration method for their application, based on quantitative metrics. Furthermore, our code can be used by others to evaluate novel methods.

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Comparison of Point Cloud Registration Techniques on Scanned Physical Objects

Denayer,

De Winter,

Bernardes

et al. 2024

Sensors

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Edge-guided generative network with attention for point cloud completion

Li,

Zhang,

Zhang

et al. 2024

Vis Comput

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Partial point cloud registration algorithm based on deep learning and non-corresponding point estimation

Cited by 2 publications

References 39 publications

Comparison of Point Cloud Registration Techniques on Scanned Physical Objects

Comparison of Point Cloud Registration Techniques on Scanned Physical Objects

Edge-guided generative network with attention for point cloud completion

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