Sparse Cross-scale Attention Network for Efficient LiDAR Panoptic Segmentation Article Swipe
Shuangjie Xu
,
Rui Wan
,
Maosheng Ye
,
Xiaoyi Zou
,
Tongyi Cao
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.05972
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2201.05972
Two major challenges of 3D LiDAR Panoptic Segmentation (PS) are that point clouds of an object are surface-aggregated and thus hard to model the long-range dependency especially for large instances, and that objects are too close to separate each other. Recent literature addresses these problems by time-consuming grouping processes such as dual-clustering, mean-shift offsets, etc., or by bird-eye-view (BEV) dense centroid representation that downplays geometry. However, the long-range geometry relationship has not been sufficiently modeled by local feature learning from the above methods. To this end, we present SCAN, a novel sparse cross-scale attention network to first align multi-scale sparse features with global voxel-encoded attention to capture the long-range relationship of instance context, which can boost the regression accuracy of the over-segmented large objects. For the surface-aggregated points, SCAN adopts a novel sparse class-agnostic representation of instance centroids, which can not only maintain the sparsity of aligned features to solve the under-segmentation on small objects, but also reduce the computation amount of the network through sparse convolution. Our method outperforms previous methods by a large margin in the SemanticKITTI dataset for the challenging 3D PS task, achieving 1st place with a real-time inference speed.
Related Topics
Concepts
Computer science
Artificial intelligence
Segmentation
Context (archaeology)
Inference
Pattern recognition (psychology)
Cluster analysis
Point cloud
Feature (linguistics)
Sparse approximation
Margin (machine learning)
Scale (ratio)
Lidar
Range (aeronautics)
Centroid
Representation (politics)
Computer vision
Machine learning
Geography
Remote sensing
Archaeology
Philosophy
Linguistics
Cartography
Politics
Law
Materials science
Composite material
Political science
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2201.05972
- https://arxiv.org/pdf/2201.05972
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312014657
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312014657Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2201.05972Digital Object Identifier
- Title
-
Sparse Cross-scale Attention Network for Efficient LiDAR Panoptic SegmentationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-16Full publication date if available
- Authors
-
Shuangjie Xu, Rui Wan, Maosheng Ye, Xiaoyi Zou, Tongyi CaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2201.05972Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2201.05972Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2201.05972Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Segmentation, Context (archaeology), Inference, Pattern recognition (psychology), Cluster analysis, Point cloud, Feature (linguistics), Sparse approximation, Margin (machine learning), Scale (ratio), Lidar, Range (aeronautics), Centroid, Representation (politics), Computer vision, Machine learning, Geography, Remote sensing, Archaeology, Philosophy, Linguistics, Cartography, Politics, Law, Materials science, Composite material, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.source.display_name | arXiv (Cornell University) |
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| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2201.05972 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
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| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2201.05972 |
| publication_date | 2022-01-16 |
| publication_year | 2022 |
| referenced_works_count | 0 |
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