Revisiting A Simple MLP Framework for Z-axis Rotation-invariant Point Cloud Place Recognition Article Swipe
Junwei Wu
,
Jiejie Liu
,
Mingjie Sun
,
Chenru Jiang
,
Jeremy S. Smith
,
Eng Gee Lim
,
Quan Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4859169/v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-4859169/v1
The objective of point cloud place recognition is to convert a point cloud into a global descriptor that can be utilized in autonomous driving applications to identify the best-matched road scene from an extensive dataset. However, capturing a point cloud from an arbitrary view by robots or self-driving vehicles often involves scene rotations, making existing deep learning-based methods susceptible to errors.To quantify this performance degradation, we introduce a novel metric: Average Recall@N under arbitrary rotations, denoted as ''R-AR@N''. To address this issue, we propose a Geometrical Transformation Module designed to convert rotation-sensitive coordinates into rotation-invariant representations. Additionally, we observe that the design of overly complex networks may not be crucial for effective point cloud analysis. In line with the straightforward architectural design of PointMLP \cite{pointmlp}, we introduce a Local Feature Transformation module that utilizes statistical representations to transform local point features within a reasonable range. This enables the network to capture diverse geometric structures and generate a robust global descriptor.Our proposed method undergoes extensive evaluation on the Oxford outdoor dataset and three in-house datasets, demonstrating an improvement of at least 2\% over previous methods on the newly proposed ''R-AR@N'' metric. Our code is available at https://github.com/jasonwjw/RI-PointMLP
Related Topics
Concepts
Point cloud
Computer science
Invariant (physics)
Rotation (mathematics)
Transformation (genetics)
Rigid transformation
Artificial intelligence
Point (geometry)
Cloud computing
Metric (unit)
Algorithm
Computer vision
Mathematics
Geometry
Operating system
Mathematical physics
Biochemistry
Gene
Operations management
Economics
Chemistry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-4859169/v1
- https://www.researchsquare.com/article/rs-4859169/latest.pdf
- OA Status
- gold
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402039060
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402039060Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-4859169/v1Digital Object Identifier
- Title
-
Revisiting A Simple MLP Framework for Z-axis Rotation-invariant Point Cloud Place RecognitionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-30Full publication date if available
- Authors
-
Junwei Wu, Jiejie Liu, Mingjie Sun, Chenru Jiang, Jeremy S. Smith, Eng Gee Lim, Quan ZhangList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-4859169/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-4859169/latest.pdfDirect 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.researchsquare.com/article/rs-4859169/latest.pdfDirect OA link when available
- Concepts
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Point cloud, Computer science, Invariant (physics), Rotation (mathematics), Transformation (genetics), Rigid transformation, Artificial intelligence, Point (geometry), Cloud computing, Metric (unit), Algorithm, Computer vision, Mathematics, Geometry, Operating system, Mathematical physics, Biochemistry, Gene, Operations management, Economics, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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29Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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