PCRNet+: A Point Cloud Alignment Algorithm Introducing Dynamic Graph Convolutional Neural Networks Article Swipe
T. Y. Qi
,
Yingchun Li
,
Jing Tian
,
Hang Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14050972
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14050972
In this paper, an improved point cloud alignment network based on PCRNet is proposed. In the improved model, DGCNN is used as a feature extractor to capture the local and global geometric features of the point cloud, and a fully connected layer is used for feature fusion and rigid-body transformation parameter prediction. Compared with the original PCRNet, the improved network shows higher accuracy and robustness in the point cloud alignment task. In order to verify the performance of the improved network, two classical algorithms, ICP and FGR, are used as benchmarks in our experiment, and experimental comparisons of PCRNet and its improved version are performed under noise-free and noise-containing conditions, respectively. The experimental results show that the improved network (PCRNet+) proposed in our approach outperforms the original PCRNet under different test conditions, including experiments conducted in noise-free, noise-containing, and occlusion scenarios. Specifically, under noise-containing conditions, PCRNet+ surpasses the next-best algorithm, FGR, by over 95.9% across three key metrics. In occlusion scenarios, PCRNet+ achieves more than 100% improvement in all evaluated metrics compared to FGR.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics14050972
- https://www.mdpi.com/2079-9292/14/5/972/pdf?version=1740733140
- OA Status
- gold
- Cited By
- 2
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408051546
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408051546Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics14050972Digital Object Identifier
- Title
-
PCRNet+: A Point Cloud Alignment Algorithm Introducing Dynamic Graph Convolutional Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-28Full publication date if available
- Authors
-
T. Y. Qi, Yingchun Li, Jing Tian, Hang ChenList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics14050972Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/14/5/972/pdf?version=1740733140Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2079-9292/14/5/972/pdf?version=1740733140Direct OA link when available
- Concepts
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Convolutional neural network, Computer science, Point cloud, Cloud computing, Algorithm, Graph, Artificial intelligence, Theoretical computer science, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
- References (count)
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20Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.achieves | 162 |
| abstract_inverted_index.approach | 123 |
| abstract_inverted_index.compared | 171 |
| abstract_inverted_index.features | 32 |
| abstract_inverted_index.improved | 4, 16, 58, 79, 101, 117 |
| abstract_inverted_index.metrics. | 157 |
| abstract_inverted_index.network, | 80 |
| abstract_inverted_index.original | 55, 126 |
| abstract_inverted_index.proposed | 120 |
| abstract_inverted_index.(PCRNet+) | 119 |
| abstract_inverted_index.alignment | 7, 69 |
| abstract_inverted_index.classical | 82 |
| abstract_inverted_index.conducted | 134 |
| abstract_inverted_index.connected | 40 |
| abstract_inverted_index.different | 129 |
| abstract_inverted_index.evaluated | 169 |
| abstract_inverted_index.extractor | 24 |
| abstract_inverted_index.geometric | 31 |
| abstract_inverted_index.including | 132 |
| abstract_inverted_index.next-best | 148 |
| abstract_inverted_index.occlusion | 139, 159 |
| abstract_inverted_index.parameter | 50 |
| abstract_inverted_index.performed | 104 |
| abstract_inverted_index.proposed. | 13 |
| abstract_inverted_index.surpasses | 146 |
| abstract_inverted_index.algorithm, | 149 |
| abstract_inverted_index.benchmarks | 90 |
| abstract_inverted_index.noise-free | 106 |
| abstract_inverted_index.rigid-body | 48 |
| abstract_inverted_index.robustness | 64 |
| abstract_inverted_index.scenarios, | 160 |
| abstract_inverted_index.scenarios. | 140 |
| abstract_inverted_index.algorithms, | 83 |
| abstract_inverted_index.comparisons | 96 |
| abstract_inverted_index.conditions, | 109, 131, 144 |
| abstract_inverted_index.experiment, | 93 |
| abstract_inverted_index.experiments | 133 |
| abstract_inverted_index.improvement | 166 |
| abstract_inverted_index.noise-free, | 136 |
| abstract_inverted_index.outperforms | 124 |
| abstract_inverted_index.performance | 76 |
| abstract_inverted_index.prediction. | 51 |
| abstract_inverted_index.experimental | 95, 112 |
| abstract_inverted_index.Specifically, | 141 |
| abstract_inverted_index.respectively. | 110 |
| abstract_inverted_index.transformation | 49 |
| abstract_inverted_index.noise-containing | 108, 143 |
| abstract_inverted_index.noise-containing, | 137 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.91670363 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |