AlignFusionNet: Efficient Cross-Modal Alignment and Fusion for 3D Semantic Occupancy Prediction Article Swipe
Ziyi Xu
,
Li Qi
,
Hai-En Du
,
Jia‐Qi Yang
,
Zhenglin Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3589858
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3589858
The environmental perception system is a critical component of autonomous vehicles, and multimodal perception systems significantly enhance perception capabilities by integrating camera and LiDAR data. This paper proposes a novel framework, AlignFusionNet. It effectively combines image and point cloud data to construct an occupancy network, thereby improving target detection and representation. The framework introduces two innovative modules: a point-level data alignment module based on geometric transformations and an enhanced fusion module utilizing cross-attention mechanisms. These modules achieve precise point-level alignment and seamless feature fusion between point clouds and RGB images. Experiments on the nuScenes-Occupancy dataset demonstrate that the proposed AlignFusionNet outperforms baseline methods, achieving a significant 15.9% improvement in mIoU and a 4% increase in IoU. Compared to the previous state-of-the-art method, OccGen, mIoU is improved by 5.9%. Further qualitative visualization analysis shows that the proposed method achieves higher representation accuracy for small objects.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3589858
- OA Status
- gold
- Cited By
- 8
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4412619188Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2025.3589858Digital Object Identifier
- Title
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AlignFusionNet: Efficient Cross-Modal Alignment and Fusion for 3D Semantic Occupancy PredictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Ziyi Xu, Li Qi, Hai-En Du, Jia‐Qi Yang, Zhenglin ChenList of authors in order
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https://doi.org/10.1109/access.2025.3589858Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3589858Direct OA link when available
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Occupancy, Computer science, Modal, Fusion, Artificial intelligence, Natural language processing, Data mining, Engineering, Philosophy, Linguistics, Architectural engineering, Chemistry, Polymer chemistryTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2025: 8Per-year citation counts (last 5 years)
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88Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4387245323, https://openalex.org/W4391935169, https://openalex.org/W4400646215, https://openalex.org/W3034653753, https://openalex.org/W4404132941, https://openalex.org/W3094632837, https://openalex.org/W4390075007, https://openalex.org/W4390872444, https://openalex.org/W6852775642, https://openalex.org/W6850325899, https://openalex.org/W3165793043, https://openalex.org/W3217305714, https://openalex.org/W4386132187, https://openalex.org/W2980040548, https://openalex.org/W2557465155, https://openalex.org/W2345333930, https://openalex.org/W3215584334, https://openalex.org/W4312894406, https://openalex.org/W4312641958, https://openalex.org/W4386083035, https://openalex.org/W6858648908, https://openalex.org/W4405785410, https://openalex.org/W6861012058, https://openalex.org/W6879916760, https://openalex.org/W6857295974, https://openalex.org/W4390872638, https://openalex.org/W4390873564, https://openalex.org/W2336416123, https://openalex.org/W4220795836, https://openalex.org/W4386065389, https://openalex.org/W4393159171, https://openalex.org/W4391013633, https://openalex.org/W4393159587, https://openalex.org/W6857082823, https://openalex.org/W4389666133, https://openalex.org/W6858172842, https://openalex.org/W4399207465, https://openalex.org/W4383108631, https://openalex.org/W4312564076, https://openalex.org/W2963926543, https://openalex.org/W4383097531, https://openalex.org/W6780075094, https://openalex.org/W4405717395, https://openalex.org/W4311763314, https://openalex.org/W4383899715, https://openalex.org/W4403295699, https://openalex.org/W4376870884, https://openalex.org/W3170030651, https://openalex.org/W3108426750, https://openalex.org/W3135991645, https://openalex.org/W6838956374, https://openalex.org/W3172863135, https://openalex.org/W4403730128, https://openalex.org/W3035574168, https://openalex.org/W4225986494, https://openalex.org/W6842385943, https://openalex.org/W4383112451, https://openalex.org/W4390590924, https://openalex.org/W2767290858, https://openalex.org/W2619383789, https://openalex.org/W2587989515, https://openalex.org/W4386768890, https://openalex.org/W3168463823, https://openalex.org/W3035172746, https://openalex.org/W4402727396, https://openalex.org/W4396214375, https://openalex.org/W6811230113, https://openalex.org/W3034868495, https://openalex.org/W3035308182, https://openalex.org/W3126856052, https://openalex.org/W3174692508, https://openalex.org/W4394708612, https://openalex.org/W4404725701, https://openalex.org/W2991216808, https://openalex.org/W3198790326, https://openalex.org/W4385574728, https://openalex.org/W4386160116, https://openalex.org/W4394621877, https://openalex.org/W4402753757, https://openalex.org/W4410342737, https://openalex.org/W4401414722, https://openalex.org/W4226305814, https://openalex.org/W3036452913, https://openalex.org/W4405784929, https://openalex.org/W4401414004, https://openalex.org/W4396596928, https://openalex.org/W4405907309, https://openalex.org/W4390961328 |
| referenced_works_count | 88 |
| abstract_inverted_index.a | 5, 28, 57, 104, 111 |
| abstract_inverted_index.4% | 112 |
| abstract_inverted_index.It | 32 |
| abstract_inverted_index.an | 42, 67 |
| abstract_inverted_index.by | 19, 126 |
| abstract_inverted_index.in | 108, 114 |
| abstract_inverted_index.is | 4, 124 |
| abstract_inverted_index.of | 8 |
| abstract_inverted_index.on | 63, 91 |
| abstract_inverted_index.to | 40, 117 |
| abstract_inverted_index.RGB | 88 |
| abstract_inverted_index.The | 0, 51 |
| abstract_inverted_index.and | 11, 22, 36, 49, 66, 80, 87, 110 |
| abstract_inverted_index.for | 141 |
| abstract_inverted_index.the | 92, 97, 118, 134 |
| abstract_inverted_index.two | 54 |
| abstract_inverted_index.IoU. | 115 |
| abstract_inverted_index.This | 25 |
| abstract_inverted_index.data | 39, 59 |
| abstract_inverted_index.mIoU | 109, 123 |
| abstract_inverted_index.that | 96, 133 |
| abstract_inverted_index.15.9% | 106 |
| abstract_inverted_index.5.9%. | 127 |
| abstract_inverted_index.LiDAR | 23 |
| abstract_inverted_index.These | 74 |
| abstract_inverted_index.based | 62 |
| abstract_inverted_index.cloud | 38 |
| abstract_inverted_index.data. | 24 |
| abstract_inverted_index.image | 35 |
| abstract_inverted_index.novel | 29 |
| abstract_inverted_index.paper | 26 |
| abstract_inverted_index.point | 37, 85 |
| abstract_inverted_index.shows | 132 |
| abstract_inverted_index.small | 142 |
| abstract_inverted_index.camera | 21 |
| abstract_inverted_index.clouds | 86 |
| abstract_inverted_index.fusion | 69, 83 |
| abstract_inverted_index.higher | 138 |
| abstract_inverted_index.method | 136 |
| abstract_inverted_index.module | 61, 70 |
| abstract_inverted_index.system | 3 |
| abstract_inverted_index.target | 47 |
| abstract_inverted_index.Further | 128 |
| abstract_inverted_index.OccGen, | 122 |
| abstract_inverted_index.achieve | 76 |
| abstract_inverted_index.between | 84 |
| abstract_inverted_index.dataset | 94 |
| abstract_inverted_index.enhance | 16 |
| abstract_inverted_index.feature | 82 |
| abstract_inverted_index.images. | 89 |
| abstract_inverted_index.method, | 121 |
| abstract_inverted_index.modules | 75 |
| abstract_inverted_index.precise | 77 |
| abstract_inverted_index.systems | 14 |
| abstract_inverted_index.thereby | 45 |
| abstract_inverted_index.Compared | 116 |
| abstract_inverted_index.accuracy | 140 |
| abstract_inverted_index.achieves | 137 |
| abstract_inverted_index.analysis | 131 |
| abstract_inverted_index.baseline | 101 |
| abstract_inverted_index.combines | 34 |
| abstract_inverted_index.critical | 6 |
| abstract_inverted_index.enhanced | 68 |
| abstract_inverted_index.improved | 125 |
| abstract_inverted_index.increase | 113 |
| abstract_inverted_index.methods, | 102 |
| abstract_inverted_index.modules: | 56 |
| abstract_inverted_index.network, | 44 |
| abstract_inverted_index.objects. | 143 |
| abstract_inverted_index.previous | 119 |
| abstract_inverted_index.proposed | 98, 135 |
| abstract_inverted_index.proposes | 27 |
| abstract_inverted_index.seamless | 81 |
| abstract_inverted_index.achieving | 103 |
| abstract_inverted_index.alignment | 60, 79 |
| abstract_inverted_index.component | 7 |
| abstract_inverted_index.construct | 41 |
| abstract_inverted_index.detection | 48 |
| abstract_inverted_index.framework | 52 |
| abstract_inverted_index.geometric | 64 |
| abstract_inverted_index.improving | 46 |
| abstract_inverted_index.occupancy | 43 |
| abstract_inverted_index.utilizing | 71 |
| abstract_inverted_index.vehicles, | 10 |
| abstract_inverted_index.autonomous | 9 |
| abstract_inverted_index.framework, | 30 |
| abstract_inverted_index.innovative | 55 |
| abstract_inverted_index.introduces | 53 |
| abstract_inverted_index.multimodal | 12 |
| abstract_inverted_index.perception | 2, 13, 17 |
| abstract_inverted_index.Experiments | 90 |
| abstract_inverted_index.demonstrate | 95 |
| abstract_inverted_index.effectively | 33 |
| abstract_inverted_index.improvement | 107 |
| abstract_inverted_index.integrating | 20 |
| abstract_inverted_index.mechanisms. | 73 |
| abstract_inverted_index.outperforms | 100 |
| abstract_inverted_index.point-level | 58, 78 |
| abstract_inverted_index.qualitative | 129 |
| abstract_inverted_index.significant | 105 |
| abstract_inverted_index.capabilities | 18 |
| abstract_inverted_index.environmental | 1 |
| abstract_inverted_index.significantly | 15 |
| abstract_inverted_index.visualization | 130 |
| abstract_inverted_index.AlignFusionNet | 99 |
| abstract_inverted_index.representation | 139 |
| abstract_inverted_index.AlignFusionNet. | 31 |
| abstract_inverted_index.cross-attention | 72 |
| abstract_inverted_index.representation. | 50 |
| abstract_inverted_index.transformations | 65 |
| abstract_inverted_index.state-of-the-art | 120 |
| abstract_inverted_index.nuScenes-Occupancy | 93 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.99565333 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |