Asymmetric Network Based on Feedback and Transformer for Multispectral LiDAR Point Cloud Semantic Segmentation Article Swipe
Zhiwen Zhang
,
Yuanxi Peng
,
Qi Zhang
,
Teng Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3597947
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3597947
The 3-D point cloud semantic segmentation extends the development of computer vision. Accurate point cloud semantic segmentation is a fundamental problem in point cloud applications. However, effective point cloud semantic segmentation is still a competitive problem due to the disorder and irregularity of point clouds. Witnessing the success of the transformer structure in natural language processing and 2-D computer vision, researchers have introduced the transformer into point cloud semantic segmentation and conducted many helpful explorations. The transformer is characterized by global modeling, and it is worth exploring how to obtain the local spatial inductive bias. In this article, we design an asymmetric network that simultaneously extracts local and global features. The network structure consists of two branches: a pointwise convolutional network with a feedback mechanism and a transformer structure based on multiscale pooling. The cascaded asymmetric network structures are used for point cloud semantic segmentation. We validate the effectiveness of the proposed network on a multispectral light detection and ranging point cloud dataset. In addition, we also conduct a series of experiments to explore the effectiveness of different structures. The proposed method achieves state-of-the-art performance compared with previously designed point cloud semantic segmentation networks.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2025.3597947
- OA Status
- gold
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413156254
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413156254Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jstars.2025.3597947Digital Object Identifier
- Title
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Asymmetric Network Based on Feedback and Transformer for Multispectral LiDAR Point Cloud Semantic SegmentationWork 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
-
Zhiwen Zhang, Yuanxi Peng, Qi Zhang, Teng LiList of authors in order
- Landing page
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https://doi.org/10.1109/jstars.2025.3597947Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/jstars.2025.3597947Direct OA link when available
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Lidar, Computer science, Multispectral image, Segmentation, Point cloud, Remote sensing, Transformer, Artificial intelligence, Computer vision, Geology, Physics, Quantum mechanics, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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58Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2917081331, https://openalex.org/W4391450838, https://openalex.org/W4408144943, https://openalex.org/W2015682345, https://openalex.org/W2755907046, https://openalex.org/W2583041318, https://openalex.org/W3127831797, https://openalex.org/W4385245566, https://openalex.org/W4391998027, https://openalex.org/W4392693773, https://openalex.org/W4394817488, https://openalex.org/W4392351523, https://openalex.org/W4407900325, https://openalex.org/W4406259389, https://openalex.org/W4409325900, https://openalex.org/W4406753873, https://openalex.org/W2211722331, https://openalex.org/W1920022804, https://openalex.org/W2798777114, https://openalex.org/W2810240468, https://openalex.org/W2797997528, https://openalex.org/W1644641054, https://openalex.org/W2798998662, https://openalex.org/W4220840476, https://openalex.org/W3034681945, https://openalex.org/W3158405343, https://openalex.org/W3173271937, https://openalex.org/W3012015905, https://openalex.org/W2979750740, https://openalex.org/W2981199548, https://openalex.org/W2991084087, https://openalex.org/W2963057320, https://openalex.org/W4402727277, https://openalex.org/W4407390748, https://openalex.org/W4382457953, https://openalex.org/W4321482463, https://openalex.org/W3044615379, https://openalex.org/W4226106508, https://openalex.org/W4313145913, https://openalex.org/W4386597031, https://openalex.org/W4407900973, https://openalex.org/W4402667891, https://openalex.org/W4407168538, https://openalex.org/W3081299251, https://openalex.org/W2472003871, https://openalex.org/W2608675201, https://openalex.org/W2982959596, https://openalex.org/W2724542434, https://openalex.org/W3090082674, https://openalex.org/W4321789806, https://openalex.org/W4225986494, https://openalex.org/W4312707458, https://openalex.org/W2113551198, https://openalex.org/W2125107731, https://openalex.org/W3111535274, https://openalex.org/W4292451836, https://openalex.org/W4214755140, https://openalex.org/W4399206244 |
| referenced_works_count | 58 |
| abstract_inverted_index.a | 18, 33, 117, 122, 126, 154, 168 |
| abstract_inverted_index.In | 95, 163 |
| abstract_inverted_index.We | 145 |
| abstract_inverted_index.an | 100 |
| abstract_inverted_index.by | 79 |
| abstract_inverted_index.in | 21, 52 |
| abstract_inverted_index.is | 17, 31, 77, 84 |
| abstract_inverted_index.it | 83 |
| abstract_inverted_index.of | 9, 42, 48, 114, 149, 170, 176 |
| abstract_inverted_index.on | 130, 153 |
| abstract_inverted_index.to | 37, 88, 172 |
| abstract_inverted_index.we | 98, 165 |
| abstract_inverted_index.2-D | 57 |
| abstract_inverted_index.3-D | 1 |
| abstract_inverted_index.The | 0, 75, 110, 133, 179 |
| abstract_inverted_index.and | 40, 56, 70, 82, 107, 125, 158 |
| abstract_inverted_index.are | 138 |
| abstract_inverted_index.due | 36 |
| abstract_inverted_index.for | 140 |
| abstract_inverted_index.how | 87 |
| abstract_inverted_index.the | 7, 38, 46, 49, 63, 90, 147, 150, 174 |
| abstract_inverted_index.two | 115 |
| abstract_inverted_index.also | 166 |
| abstract_inverted_index.have | 61 |
| abstract_inverted_index.into | 65 |
| abstract_inverted_index.many | 72 |
| abstract_inverted_index.that | 103 |
| abstract_inverted_index.this | 96 |
| abstract_inverted_index.used | 139 |
| abstract_inverted_index.with | 121, 186 |
| abstract_inverted_index.based | 129 |
| abstract_inverted_index.bias. | 94 |
| abstract_inverted_index.cloud | 3, 14, 23, 28, 67, 142, 161, 190 |
| abstract_inverted_index.light | 156 |
| abstract_inverted_index.local | 91, 106 |
| abstract_inverted_index.point | 2, 13, 22, 27, 43, 66, 141, 160, 189 |
| abstract_inverted_index.still | 32 |
| abstract_inverted_index.worth | 85 |
| abstract_inverted_index.design | 99 |
| abstract_inverted_index.global | 80, 108 |
| abstract_inverted_index.method | 181 |
| abstract_inverted_index.obtain | 89 |
| abstract_inverted_index.series | 169 |
| abstract_inverted_index.clouds. | 44 |
| abstract_inverted_index.conduct | 167 |
| abstract_inverted_index.explore | 173 |
| abstract_inverted_index.extends | 6 |
| abstract_inverted_index.helpful | 73 |
| abstract_inverted_index.natural | 53 |
| abstract_inverted_index.network | 102, 111, 120, 136, 152 |
| abstract_inverted_index.problem | 20, 35 |
| abstract_inverted_index.ranging | 159 |
| abstract_inverted_index.spatial | 92 |
| abstract_inverted_index.success | 47 |
| abstract_inverted_index.vision, | 59 |
| abstract_inverted_index.vision. | 11 |
| abstract_inverted_index.Accurate | 12 |
| abstract_inverted_index.However, | 25 |
| abstract_inverted_index.achieves | 182 |
| abstract_inverted_index.article, | 97 |
| abstract_inverted_index.cascaded | 134 |
| abstract_inverted_index.compared | 185 |
| abstract_inverted_index.computer | 10, 58 |
| abstract_inverted_index.consists | 113 |
| abstract_inverted_index.dataset. | 162 |
| abstract_inverted_index.designed | 188 |
| abstract_inverted_index.disorder | 39 |
| abstract_inverted_index.extracts | 105 |
| abstract_inverted_index.feedback | 123 |
| abstract_inverted_index.language | 54 |
| abstract_inverted_index.pooling. | 132 |
| abstract_inverted_index.proposed | 151, 180 |
| abstract_inverted_index.semantic | 4, 15, 29, 68, 143, 191 |
| abstract_inverted_index.validate | 146 |
| abstract_inverted_index.addition, | 164 |
| abstract_inverted_index.branches: | 116 |
| abstract_inverted_index.conducted | 71 |
| abstract_inverted_index.detection | 157 |
| abstract_inverted_index.different | 177 |
| abstract_inverted_index.effective | 26 |
| abstract_inverted_index.exploring | 86 |
| abstract_inverted_index.features. | 109 |
| abstract_inverted_index.inductive | 93 |
| abstract_inverted_index.mechanism | 124 |
| abstract_inverted_index.modeling, | 81 |
| abstract_inverted_index.networks. | 193 |
| abstract_inverted_index.pointwise | 118 |
| abstract_inverted_index.structure | 51, 112, 128 |
| abstract_inverted_index.Witnessing | 45 |
| abstract_inverted_index.asymmetric | 101, 135 |
| abstract_inverted_index.introduced | 62 |
| abstract_inverted_index.multiscale | 131 |
| abstract_inverted_index.previously | 187 |
| abstract_inverted_index.processing | 55 |
| abstract_inverted_index.structures | 137 |
| abstract_inverted_index.competitive | 34 |
| abstract_inverted_index.development | 8 |
| abstract_inverted_index.experiments | 171 |
| abstract_inverted_index.fundamental | 19 |
| abstract_inverted_index.performance | 184 |
| abstract_inverted_index.researchers | 60 |
| abstract_inverted_index.structures. | 178 |
| abstract_inverted_index.transformer | 50, 64, 76, 127 |
| abstract_inverted_index.irregularity | 41 |
| abstract_inverted_index.segmentation | 5, 16, 30, 69, 192 |
| abstract_inverted_index.applications. | 24 |
| abstract_inverted_index.characterized | 78 |
| abstract_inverted_index.convolutional | 119 |
| abstract_inverted_index.effectiveness | 148, 175 |
| abstract_inverted_index.explorations. | 74 |
| abstract_inverted_index.multispectral | 155 |
| abstract_inverted_index.segmentation. | 144 |
| abstract_inverted_index.simultaneously | 104 |
| abstract_inverted_index.state-of-the-art | 183 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.37060954 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |