From One to Many: Dynamic Cross Attention Networks for LiDAR and Camera Fusion Article Swipe
Rui Wan
,
Shuangjie Xu
,
Wei Wu
,
Xiaoyi Zou
,
Tongyi Cao
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2209.12254
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2209.12254
LiDAR and cameras are two complementary sensors for 3D perception in autonomous driving. LiDAR point clouds have accurate spatial and geometry information, while RGB images provide textural and color data for context reasoning. To exploit LiDAR and cameras jointly, existing fusion methods tend to align each 3D point to only one projected image pixel based on calibration, namely one-to-one mapping. However, the performance of these approaches highly relies on the calibration quality, which is sensitive to the temporal and spatial synchronization of sensors. Therefore, we propose a Dynamic Cross Attention (DCA) module with a novel one-to-many cross-modality mapping that learns multiple offsets from the initial projection towards the neighborhood and thus develops tolerance to calibration error. Moreover, a \textit{dynamic query enhancement} is proposed to perceive the model-independent calibration, which further strengthens DCA's tolerance to the initial misalignment. The whole fusion architecture named Dynamic Cross Attention Network (DCAN) exploits multi-level image features and adapts to multiple representations of point clouds, which allows DCA to serve as a plug-in fusion module. Extensive experiments on nuScenes and KITTI prove DCA's effectiveness. The proposed DCAN outperforms state-of-the-art methods on the nuScenes detection challenge.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2209.12254
- https://arxiv.org/pdf/2209.12254
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4297435512
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4297435512Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2209.12254Digital Object Identifier
- Title
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From One to Many: Dynamic Cross Attention Networks for LiDAR and Camera FusionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-25Full publication date if available
- Authors
-
Rui Wan, Shuangjie Xu, Wei Wu, Xiaoyi Zou, Tongyi CaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2209.12254Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2209.12254Direct 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/2209.12254Direct OA link when available
- Concepts
-
Computer science, Lidar, Computer vision, Artificial intelligence, Point cloud, Context (archaeology), Calibration, Exploit, Sensor fusion, Fusion, Remote sensing, Geography, Mathematics, Philosophy, Linguistics, Computer security, Statistics, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.towards | 106 |
| abstract_inverted_index.However, | 60 |
| abstract_inverted_index.accurate | 17 |
| abstract_inverted_index.develops | 111 |
| abstract_inverted_index.driving. | 12 |
| abstract_inverted_index.existing | 39 |
| abstract_inverted_index.exploits | 147 |
| abstract_inverted_index.features | 150 |
| abstract_inverted_index.geometry | 20 |
| abstract_inverted_index.jointly, | 38 |
| abstract_inverted_index.mapping. | 59 |
| abstract_inverted_index.multiple | 100, 154 |
| abstract_inverted_index.nuScenes | 172, 186 |
| abstract_inverted_index.perceive | 124 |
| abstract_inverted_index.proposed | 122, 179 |
| abstract_inverted_index.quality, | 71 |
| abstract_inverted_index.sensors. | 82 |
| abstract_inverted_index.temporal | 77 |
| abstract_inverted_index.textural | 26 |
| abstract_inverted_index.Attention | 89, 144 |
| abstract_inverted_index.Extensive | 169 |
| abstract_inverted_index.Moreover, | 116 |
| abstract_inverted_index.detection | 187 |
| abstract_inverted_index.projected | 51 |
| abstract_inverted_index.sensitive | 74 |
| abstract_inverted_index.tolerance | 112, 132 |
| abstract_inverted_index.Therefore, | 83 |
| abstract_inverted_index.approaches | 65 |
| abstract_inverted_index.autonomous | 11 |
| abstract_inverted_index.challenge. | 188 |
| abstract_inverted_index.one-to-one | 58 |
| abstract_inverted_index.perception | 9 |
| abstract_inverted_index.projection | 105 |
| abstract_inverted_index.reasoning. | 32 |
| abstract_inverted_index.calibration | 70, 114 |
| abstract_inverted_index.experiments | 170 |
| abstract_inverted_index.multi-level | 148 |
| abstract_inverted_index.one-to-many | 95 |
| abstract_inverted_index.outperforms | 181 |
| abstract_inverted_index.performance | 62 |
| abstract_inverted_index.strengthens | 130 |
| abstract_inverted_index.architecture | 140 |
| abstract_inverted_index.calibration, | 56, 127 |
| abstract_inverted_index.enhancement} | 120 |
| abstract_inverted_index.information, | 21 |
| abstract_inverted_index.neighborhood | 108 |
| abstract_inverted_index.complementary | 5 |
| abstract_inverted_index.misalignment. | 136 |
| abstract_inverted_index.cross-modality | 96 |
| abstract_inverted_index.effectiveness. | 177 |
| abstract_inverted_index.\textit{dynamic | 118 |
| abstract_inverted_index.representations | 155 |
| abstract_inverted_index.synchronization | 80 |
| abstract_inverted_index.state-of-the-art | 182 |
| abstract_inverted_index.model-independent | 126 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile |