mm-CasGAN: A cascaded adversarial neural framework for mmWave radar point cloud enhancement Article Swipe
Kareeb Hasan
,
Beng Oh
,
Nithurshan Nadarajah
,
Mehmet Rasit Yuce
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.inffus.2024.102388
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.inffus.2024.102388
Handling and interpreting sparse 3D point clouds, especially from mmWave radar, presents unique challenges due to the inherent data sparsity and the vast domain difference compared to denser point clouds like those from LiDAR. In this paper, we introduce a novel cascaded generative adversarial network (GAN) approach to bridge this domain gap. The core principle is to progressively refine the radar-based point cloud through a series of GANs, each targeting a higher resolution. By leveraging multi-level features and a hybrid loss function that combines adversarial, geometric, and consistency components, our method ensures a smooth transition from the sparse radar representation to a high-resolution LiDAR-like point cloud. Our cascaded approach operates at a patch level, and the integrated loss function ensures that the generated points not only resemble the target domain but also maintain geometric and structural fidelity. Real-life dataset consisting mostly of moving pedestrians were collected using a system made of Radar, LiDAR, and RGB Camera. Through an extensive experiment on the collected real-world pedestrian dataset, we validate the efficacy of our approach. Inference from the network indicates that our method can upsample mmWave radar point clouds with enhanced density, uniformity, and closer alignment to the ground truth LiDAR point clouds, which is the first of its kind network to do so.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.inffus.2024.102388
- OA Status
- hybrid
- Cited By
- 4
- References
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4393281956Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.inffus.2024.102388Digital Object Identifier
- Title
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mm-CasGAN: A cascaded adversarial neural framework for mmWave radar point cloud enhancementWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-28Full publication date if available
- Authors
-
Kareeb Hasan, Beng Oh, Nithurshan Nadarajah, Mehmet Rasit YuceList of authors in order
- Landing page
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https://doi.org/10.1016/j.inffus.2024.102388Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.inffus.2024.102388Direct OA link when available
- Concepts
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Computer science, Point cloud, Radar, Cloud computing, Adversarial system, Point (geometry), Remote sensing, Telecommunications, Artificial intelligence, Geology, Geometry, Mathematics, Operating systemTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.from | 8, 32, 95, 174 |
| abstract_inverted_index.gap. | 51 |
| abstract_inverted_index.kind | 207 |
| abstract_inverted_index.like | 30 |
| abstract_inverted_index.loss | 80, 117 |
| abstract_inverted_index.made | 149 |
| abstract_inverted_index.only | 125 |
| abstract_inverted_index.that | 82, 120, 178 |
| abstract_inverted_index.this | 35, 49 |
| abstract_inverted_index.vast | 22 |
| abstract_inverted_index.were | 144 |
| abstract_inverted_index.with | 187 |
| abstract_inverted_index.(GAN) | 45 |
| abstract_inverted_index.GANs, | 67 |
| abstract_inverted_index.LiDAR | 198 |
| abstract_inverted_index.cloud | 62 |
| abstract_inverted_index.first | 204 |
| abstract_inverted_index.novel | 40 |
| abstract_inverted_index.patch | 112 |
| abstract_inverted_index.point | 5, 28, 61, 104, 185, 199 |
| abstract_inverted_index.radar | 98, 184 |
| abstract_inverted_index.those | 31 |
| abstract_inverted_index.truth | 197 |
| abstract_inverted_index.using | 146 |
| abstract_inverted_index.which | 201 |
| abstract_inverted_index.LiDAR, | 152 |
| abstract_inverted_index.LiDAR. | 33 |
| abstract_inverted_index.Radar, | 151 |
| abstract_inverted_index.bridge | 48 |
| abstract_inverted_index.closer | 192 |
| abstract_inverted_index.cloud. | 105 |
| abstract_inverted_index.clouds | 29, 186 |
| abstract_inverted_index.denser | 27 |
| abstract_inverted_index.domain | 23, 50, 129 |
| abstract_inverted_index.ground | 196 |
| abstract_inverted_index.higher | 71 |
| abstract_inverted_index.hybrid | 79 |
| abstract_inverted_index.level, | 113 |
| abstract_inverted_index.method | 90, 180 |
| abstract_inverted_index.mmWave | 9, 183 |
| abstract_inverted_index.mostly | 140 |
| abstract_inverted_index.moving | 142 |
| abstract_inverted_index.paper, | 36 |
| abstract_inverted_index.points | 123 |
| abstract_inverted_index.radar, | 10 |
| abstract_inverted_index.refine | 58 |
| abstract_inverted_index.series | 65 |
| abstract_inverted_index.smooth | 93 |
| abstract_inverted_index.sparse | 3, 97 |
| abstract_inverted_index.system | 148 |
| abstract_inverted_index.target | 128 |
| abstract_inverted_index.unique | 12 |
| abstract_inverted_index.Camera. | 155 |
| abstract_inverted_index.Through | 156 |
| abstract_inverted_index.clouds, | 6, 200 |
| abstract_inverted_index.dataset | 138 |
| abstract_inverted_index.ensures | 91, 119 |
| abstract_inverted_index.network | 44, 176, 208 |
| abstract_inverted_index.through | 63 |
| abstract_inverted_index.Handling | 0 |
| abstract_inverted_index.approach | 46, 108 |
| abstract_inverted_index.cascaded | 41, 107 |
| abstract_inverted_index.combines | 83 |
| abstract_inverted_index.compared | 25 |
| abstract_inverted_index.dataset, | 165 |
| abstract_inverted_index.density, | 189 |
| abstract_inverted_index.efficacy | 169 |
| abstract_inverted_index.enhanced | 188 |
| abstract_inverted_index.features | 76 |
| abstract_inverted_index.function | 81, 118 |
| abstract_inverted_index.inherent | 17 |
| abstract_inverted_index.maintain | 132 |
| abstract_inverted_index.operates | 109 |
| abstract_inverted_index.presents | 11 |
| abstract_inverted_index.resemble | 126 |
| abstract_inverted_index.sparsity | 19 |
| abstract_inverted_index.upsample | 182 |
| abstract_inverted_index.validate | 167 |
| abstract_inverted_index.Inference | 173 |
| abstract_inverted_index.Real-life | 137 |
| abstract_inverted_index.alignment | 193 |
| abstract_inverted_index.approach. | 172 |
| abstract_inverted_index.collected | 145, 162 |
| abstract_inverted_index.extensive | 158 |
| abstract_inverted_index.fidelity. | 136 |
| abstract_inverted_index.generated | 122 |
| abstract_inverted_index.geometric | 133 |
| abstract_inverted_index.indicates | 177 |
| abstract_inverted_index.introduce | 38 |
| abstract_inverted_index.principle | 54 |
| abstract_inverted_index.targeting | 69 |
| abstract_inverted_index.LiDAR-like | 103 |
| abstract_inverted_index.challenges | 13 |
| abstract_inverted_index.consisting | 139 |
| abstract_inverted_index.difference | 24 |
| abstract_inverted_index.especially | 7 |
| abstract_inverted_index.experiment | 159 |
| abstract_inverted_index.generative | 42 |
| abstract_inverted_index.geometric, | 85 |
| abstract_inverted_index.integrated | 116 |
| abstract_inverted_index.leveraging | 74 |
| abstract_inverted_index.pedestrian | 164 |
| abstract_inverted_index.real-world | 163 |
| abstract_inverted_index.structural | 135 |
| abstract_inverted_index.transition | 94 |
| abstract_inverted_index.adversarial | 43 |
| abstract_inverted_index.components, | 88 |
| abstract_inverted_index.consistency | 87 |
| abstract_inverted_index.multi-level | 75 |
| abstract_inverted_index.pedestrians | 143 |
| abstract_inverted_index.radar-based | 60 |
| abstract_inverted_index.resolution. | 72 |
| abstract_inverted_index.uniformity, | 190 |
| abstract_inverted_index.adversarial, | 84 |
| abstract_inverted_index.interpreting | 2 |
| abstract_inverted_index.progressively | 57 |
| abstract_inverted_index.representation | 99 |
| abstract_inverted_index.high-resolution | 102 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5045660697 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I56590836 |
| citation_normalized_percentile.value | 0.73962807 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |