Cross-Domain Generalization for LiDAR-Based 3D Object Detection in Infrastructure and Vehicle Environments Article Swipe
Peng Zhi
,
Longhao Jiang
,
Xiao Yang
,
Xingzheng Wang
,
Hung-Wei Li
,
Qingguo Zhou
,
Kuan‐Ching Li
,
Mirjana Ivanović
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25030767
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/s25030767
In the intelligent transportation field, the Internet of Things (IoT) is commonly applied using 3D object detection as a crucial part of Vehicle-to-Everything (V2X) cooperative perception. However, challenges arise from discrepancies in sensor configurations between vehicles and infrastructure, leading to variations in the scale and heterogeneity of point clouds. To address the performance differences caused by the generalization problem of 3D object detection models with heterogeneous LiDAR point clouds, we propose the Dual-Channel Generalization Neural Network (DCGNN), which incorporates a novel data-level downsampling and calibration module along with a cross-perspective Squeeze-and-Excitation attention mechanism for improved feature fusion. Experimental results using the DAIR-V2X dataset indicate that DCGNN outperforms detectors trained on single datasets, demonstrating significant improvements over selected baseline models.
Related Topics
Concepts
Point cloud
Computer science
Lidar
Generalization
Artificial intelligence
Object detection
Perspective (graphical)
Channel (broadcasting)
Object (grammar)
Field (mathematics)
Computer vision
Data mining
Pattern recognition (psychology)
Remote sensing
Computer network
Geography
Mathematics
Pure mathematics
Mathematical analysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s25030767
- OA Status
- gold
- Cited By
- 2
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406910065
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406910065Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s25030767Digital Object Identifier
- Title
-
Cross-Domain Generalization for LiDAR-Based 3D Object Detection in Infrastructure and Vehicle EnvironmentsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-27Full publication date if available
- Authors
-
Peng Zhi, Longhao Jiang, Xiao Yang, Xingzheng Wang, Hung-Wei Li, Qingguo Zhou, Kuan‐Ching Li, Mirjana IvanovićList of authors in order
- Landing page
-
https://doi.org/10.3390/s25030767Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/s25030767Direct OA link when available
- Concepts
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Point cloud, Computer science, Lidar, Generalization, Artificial intelligence, Object detection, Perspective (graphical), Channel (broadcasting), Object (grammar), Field (mathematics), Computer vision, Data mining, Pattern recognition (psychology), Remote sensing, Computer network, Geography, Mathematics, Pure mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
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42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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