An efficient image-guided-based 3D point cloud moving object segmentation with transformer-attention in autonomous driving Article Swipe
Qipeng Li
,
Yuan Zhuang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.jag.2023.103488
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.jag.2023.103488
For intelligent transportation systems, moving object segmentation (MOS) provides valuable information for robots and intelligent vehicles, such as collision avoidance, path planning, and static map construction. However, all existing 3D point cloud MOS methods are based on LiDAR-only, which limits the ability to fuse supplementary information from different sensors. In this article, we solve the robust and accurate 3D MOS problem by designing a dual-stream network that integrates point clouds and images. We propose a perspective residual mechanism to mine the spatio-temporal motion information of point clouds, and design a fusion module based on Transformer Attention to extract multi-scale feature information from point clouds and images, improving the segmentation integrity of moving objects. Many experiments on the benchmark dataset show the superiority of our method. On the Semantic-KITTI, we outperform the advanced method by 6.5% mIoU. And we further apply our proposed model to the Semantic-KITTI: Moving Object Segmentation competition and achieve an advanced ranking on the leaderboard.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jag.2023.103488
- OA Status
- gold
- Cited By
- 13
- References
- 51
- Related Works
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- OpenAlex ID
- https://openalex.org/W4386780265
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386780265Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.jag.2023.103488Digital Object Identifier
- Title
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An efficient image-guided-based 3D point cloud moving object segmentation with transformer-attention in autonomous drivingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-01Full publication date if available
- Authors
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Qipeng Li, Yuan ZhuangList of authors in order
- Landing page
-
https://doi.org/10.1016/j.jag.2023.103488Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.jag.2023.103488Direct OA link when available
- Concepts
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Point cloud, Artificial intelligence, Computer vision, Segmentation, Computer science, Motion planning, Object detection, Benchmark (surveying), Robot, Geography, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 8, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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51Number 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.that | 66 |
| abstract_inverted_index.this | 50 |
| abstract_inverted_index.(MOS) | 7 |
| abstract_inverted_index.apply | 139 |
| abstract_inverted_index.based | 35, 92 |
| abstract_inverted_index.cloud | 31 |
| abstract_inverted_index.mIoU. | 135 |
| abstract_inverted_index.model | 142 |
| abstract_inverted_index.point | 30, 68, 85, 102 |
| abstract_inverted_index.solve | 53 |
| abstract_inverted_index.which | 38 |
| abstract_inverted_index.Moving | 146 |
| abstract_inverted_index.Object | 147 |
| abstract_inverted_index.clouds | 69, 103 |
| abstract_inverted_index.design | 88 |
| abstract_inverted_index.fusion | 90 |
| abstract_inverted_index.limits | 39 |
| abstract_inverted_index.method | 132 |
| abstract_inverted_index.module | 91 |
| abstract_inverted_index.motion | 82 |
| abstract_inverted_index.moving | 4, 111 |
| abstract_inverted_index.object | 5 |
| abstract_inverted_index.robots | 12 |
| abstract_inverted_index.robust | 55 |
| abstract_inverted_index.static | 23 |
| abstract_inverted_index.ability | 41 |
| abstract_inverted_index.achieve | 151 |
| abstract_inverted_index.clouds, | 86 |
| abstract_inverted_index.dataset | 118 |
| abstract_inverted_index.extract | 97 |
| abstract_inverted_index.feature | 99 |
| abstract_inverted_index.further | 138 |
| abstract_inverted_index.images, | 105 |
| abstract_inverted_index.images. | 71 |
| abstract_inverted_index.method. | 124 |
| abstract_inverted_index.methods | 33 |
| abstract_inverted_index.network | 65 |
| abstract_inverted_index.problem | 60 |
| abstract_inverted_index.propose | 73 |
| abstract_inverted_index.ranking | 154 |
| abstract_inverted_index.However, | 26 |
| abstract_inverted_index.accurate | 57 |
| abstract_inverted_index.advanced | 131, 153 |
| abstract_inverted_index.article, | 51 |
| abstract_inverted_index.existing | 28 |
| abstract_inverted_index.objects. | 112 |
| abstract_inverted_index.proposed | 141 |
| abstract_inverted_index.provides | 8 |
| abstract_inverted_index.residual | 76 |
| abstract_inverted_index.sensors. | 48 |
| abstract_inverted_index.systems, | 3 |
| abstract_inverted_index.valuable | 9 |
| abstract_inverted_index.Attention | 95 |
| abstract_inverted_index.benchmark | 117 |
| abstract_inverted_index.collision | 18 |
| abstract_inverted_index.designing | 62 |
| abstract_inverted_index.different | 47 |
| abstract_inverted_index.improving | 106 |
| abstract_inverted_index.integrity | 109 |
| abstract_inverted_index.mechanism | 77 |
| abstract_inverted_index.planning, | 21 |
| abstract_inverted_index.vehicles, | 15 |
| abstract_inverted_index.avoidance, | 19 |
| abstract_inverted_index.integrates | 67 |
| abstract_inverted_index.outperform | 129 |
| abstract_inverted_index.LiDAR-only, | 37 |
| abstract_inverted_index.Transformer | 94 |
| abstract_inverted_index.competition | 149 |
| abstract_inverted_index.dual-stream | 64 |
| abstract_inverted_index.experiments | 114 |
| abstract_inverted_index.information | 10, 45, 83, 100 |
| abstract_inverted_index.intelligent | 1, 14 |
| abstract_inverted_index.multi-scale | 98 |
| abstract_inverted_index.perspective | 75 |
| abstract_inverted_index.superiority | 121 |
| abstract_inverted_index.Segmentation | 148 |
| abstract_inverted_index.leaderboard. | 157 |
| abstract_inverted_index.segmentation | 6, 108 |
| abstract_inverted_index.construction. | 25 |
| abstract_inverted_index.supplementary | 44 |
| abstract_inverted_index.transportation | 2 |
| abstract_inverted_index.Semantic-KITTI, | 127 |
| abstract_inverted_index.Semantic-KITTI: | 145 |
| abstract_inverted_index.spatio-temporal | 81 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5066333395 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I37461747, https://openalex.org/I4210118728 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.88088462 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |