NRAP-RCNN: A Pseudo Point Cloud 3D Object Detection Method Based on Noise-Reduction Sparse Convolution and Attention Mechanism Article Swipe
Ziyue Zhou
,
Yan-Bin Jia
,
Tao Zhu
,
Yaping Wan
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/info16030176
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/info16030176
In recent years, pseudo point clouds generated from depth completion of RGB images and LiDAR data have provided a robust foundation for multimodal 3D object detection. However, the generation process often introduces noise, reducing data quality and detection accuracy. Moreover, existing methods fail to effectively capture channel correlations and global contextual information during the 2D feature extraction stage after the 3D backbone network, limiting detection performance. To address these challenges, this paper proposes NRAP-RCNN, a pseudo point cloud-based 3D object detection method with two key innovations: (1) A noise-reduction sparse convolution network (NRConvNet), comprising NRConv (noise-resistant submanifold sparse convolution), SRB (sparse convolution residual block), and MHSA (multi-head self-attention). NRConv suppresses pseudo point cloud noise by jointly encoding 2D and 3D features, SRB enhances feature extraction depth and robustness, and MHSA optimizes global feature representation. (2) An attention fusion module (ECA_GCA) is introduced to enhance the feature representation of the 2D backbone network by combining channel and global contextual information. The experimental results demonstrate that NRAP-RCNN achieves 88.4% car AP (R40) on the KITTI validation set and 85.1% on the test set, significantly outperforming advanced 3D detection methods, showcasing its effectiveness in improving detection performance.
Related Topics
Concepts
Convolution (computer science)
Point cloud
Mechanism (biology)
Noise reduction
Computer science
Noise (video)
Reduction (mathematics)
Point (geometry)
Object (grammar)
Artificial intelligence
Pattern recognition (psychology)
Computer vision
Mathematics
Physics
Geometry
Image (mathematics)
Artificial neural network
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/info16030176
- https://www.mdpi.com/2078-2489/16/3/176/pdf?version=1740565021
- OA Status
- gold
- Cited By
- 1
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407972810
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4407972810Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/info16030176Digital Object Identifier
- Title
-
NRAP-RCNN: A Pseudo Point Cloud 3D Object Detection Method Based on Noise-Reduction Sparse Convolution and Attention MechanismWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-26Full publication date if available
- Authors
-
Ziyue Zhou, Yan-Bin Jia, Tao Zhu, Yaping WanList of authors in order
- Landing page
-
https://doi.org/10.3390/info16030176Publisher landing page
- PDF URL
-
https://www.mdpi.com/2078-2489/16/3/176/pdf?version=1740565021Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2078-2489/16/3/176/pdf?version=1740565021Direct OA link when available
- Concepts
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Convolution (computer science), Point cloud, Mechanism (biology), Noise reduction, Computer science, Noise (video), Reduction (mathematics), Point (geometry), Object (grammar), Artificial intelligence, Pattern recognition (psychology), Computer vision, Mathematics, Physics, Geometry, Image (mathematics), Artificial neural network, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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55Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(ECA_GCA) | 139 |
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| abstract_inverted_index.comprising | 93 |
| abstract_inverted_index.contextual | 50, 157 |
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| abstract_inverted_index.innovations: | 85 |
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| abstract_inverted_index.representation. | 133 |
| abstract_inverted_index.(noise-resistant | 95 |
| abstract_inverted_index.self-attention). | 107 |
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