MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion Article Swipe
Peicheng Shi
,
Wenchao Wu
,
Aixi Yang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/a18030172
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/a18030172
3D object detection plays a pivotal role in achieving accurate environmental perception, particularly in complex traffic scenarios where single-modal detection methods often fail to meet precision requirements. This highlights the necessity of multi-modal fusion approaches to enhance detection performance. However, existing camera-LiDAR intermediate fusion methods suffer from insufficient interaction between local and global features and limited fine-grained feature extraction capabilities, which results in inadequate small object detection and unstable performance in complex scenes. To address these issues, the multi-modal 3D object detection algorithm with pointwise and voxelwise fusion (MPVF) is proposed, which enhances multi-modal feature interaction and optimizes feature extraction strategies to improve detection precision and robustness. First, the pointwise and voxelwise fusion (PVWF) module is proposed to combine local features from the pointwise fusion (PWF) module with global features from the voxelwise fusion (VWF) module, enhancing the interaction between features across modalities, improving small object detection capabilities, and boosting model performance in complex scenes. Second, an expressive feature extraction module, improved ResNet-101 and feature pyramid (IRFP), is developed, comprising the improved ResNet-101 (IR) and feature pyramid (FP) modules. The IR module uses a group convolution strategy to inject high-level semantic features into the PWF and VWF modules, improving extraction efficiency. The FP module, placed at an intermediate stage, captures fine-grained features at various resolutions, enhancing the model’s precision and robustness. Finally, evaluation on the KITTI dataset demonstrates a mean Average Precision (mAP) of 69.24%, a 2.75% improvement over GraphAlign++. Detection accuracy for cars, pedestrians, and cyclists reaches 85.12%, 48.61%, and 70.12%, respectively, with the proposed method excelling in pedestrian and cyclist detection.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/a18030172
- https://www.mdpi.com/1999-4893/18/3/172/pdf?version=1742373512
- OA Status
- gold
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408624624
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408624624Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/a18030172Digital Object Identifier
- Title
-
MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise FusionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-19Full publication date if available
- Authors
-
Peicheng Shi, Wenchao Wu, Aixi YangList of authors in order
- Landing page
-
https://doi.org/10.3390/a18030172Publisher landing page
- PDF URL
-
https://www.mdpi.com/1999-4893/18/3/172/pdf?version=1742373512Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1999-4893/18/3/172/pdf?version=1742373512Direct OA link when available
- Concepts
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Pointwise, Modal, Fusion, Artificial intelligence, Computer science, Algorithm, Computer vision, Object (grammar), Sensor fusion, Pattern recognition (psychology), Mathematics, Mathematical analysis, Materials science, Linguistics, Philosophy, Polymer chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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66Number 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/W2911486422, https://openalex.org/W4306701944, https://openalex.org/W3035254347, https://openalex.org/W3171032126, https://openalex.org/W4367182782, https://openalex.org/W3118341329, https://openalex.org/W4386076370, https://openalex.org/W3035461736, https://openalex.org/W2964062501, https://openalex.org/W3176287975, https://openalex.org/W3130463448, https://openalex.org/W4213176388, https://openalex.org/W2967324759, https://openalex.org/W3108426750, https://openalex.org/W2150066425, https://openalex.org/W3170984066, https://openalex.org/W3175233244, https://openalex.org/W3176319743, https://openalex.org/W4382464460, https://openalex.org/W4382450829, https://openalex.org/W3206020460, https://openalex.org/W2949708697, https://openalex.org/W3034681945, https://openalex.org/W2885285090, https://openalex.org/W2963037989, https://openalex.org/W3035346742, https://openalex.org/W3113028524, https://openalex.org/W3004237909, https://openalex.org/W2555618208, https://openalex.org/W2963400571, https://openalex.org/W3117804044, https://openalex.org/W2963727135, https://openalex.org/W2951517617, https://openalex.org/W6838956374, https://openalex.org/W2963150697, https://openalex.org/W6686211706, https://openalex.org/W2194775991, https://openalex.org/W2108598243, https://openalex.org/W2981857055, https://openalex.org/W3204217726, https://openalex.org/W4382460130, https://openalex.org/W4316876965, https://openalex.org/W4385835750, https://openalex.org/W4389776357, https://openalex.org/W4386083121, https://openalex.org/W4385975752, https://openalex.org/W4403780499, https://openalex.org/W4401023960, https://openalex.org/W3034407526, https://openalex.org/W4390873008, https://openalex.org/W4402704555, https://openalex.org/W2897529137, https://openalex.org/W2981949127, https://openalex.org/W6772253573, https://openalex.org/W3034602892, https://openalex.org/W3034314779, https://openalex.org/W3201719054, https://openalex.org/W3159695738, https://openalex.org/W4376466999, https://openalex.org/W3204415296, https://openalex.org/W6805116906, https://openalex.org/W4318833281, https://openalex.org/W4281773951, https://openalex.org/W3008105217, https://openalex.org/W2184393491, https://openalex.org/W4200629389 |
| referenced_works_count | 66 |
| abstract_inverted_index.a | 4, 183, 228, 235 |
| abstract_inverted_index.3D | 0, 79 |
| abstract_inverted_index.FP | 202 |
| abstract_inverted_index.IR | 180 |
| abstract_inverted_index.To | 73 |
| abstract_inverted_index.an | 156, 206 |
| abstract_inverted_index.at | 205, 212 |
| abstract_inverted_index.in | 7, 13, 62, 70, 152, 258 |
| abstract_inverted_index.is | 89, 115, 167 |
| abstract_inverted_index.of | 31, 233 |
| abstract_inverted_index.on | 223 |
| abstract_inverted_index.to | 23, 35, 101, 117, 187 |
| abstract_inverted_index.PWF | 194 |
| abstract_inverted_index.The | 179, 201 |
| abstract_inverted_index.VWF | 196 |
| abstract_inverted_index.and | 51, 54, 67, 85, 96, 105, 110, 148, 163, 174, 195, 219, 245, 250, 260 |
| abstract_inverted_index.for | 242 |
| abstract_inverted_index.the | 29, 77, 108, 122, 131, 137, 170, 193, 216, 224, 254 |
| abstract_inverted_index.(FP) | 177 |
| abstract_inverted_index.(IR) | 173 |
| abstract_inverted_index.This | 27 |
| abstract_inverted_index.fail | 22 |
| abstract_inverted_index.from | 46, 121, 130 |
| abstract_inverted_index.into | 192 |
| abstract_inverted_index.mean | 229 |
| abstract_inverted_index.meet | 24 |
| abstract_inverted_index.over | 238 |
| abstract_inverted_index.role | 6 |
| abstract_inverted_index.uses | 182 |
| abstract_inverted_index.with | 83, 127, 253 |
| abstract_inverted_index.(PWF) | 125 |
| abstract_inverted_index.(VWF) | 134 |
| abstract_inverted_index.(mAP) | 232 |
| abstract_inverted_index.2.75% | 236 |
| abstract_inverted_index.KITTI | 225 |
| abstract_inverted_index.cars, | 243 |
| abstract_inverted_index.group | 184 |
| abstract_inverted_index.local | 50, 119 |
| abstract_inverted_index.model | 150 |
| abstract_inverted_index.often | 21 |
| abstract_inverted_index.plays | 3 |
| abstract_inverted_index.small | 64, 144 |
| abstract_inverted_index.these | 75 |
| abstract_inverted_index.where | 17 |
| abstract_inverted_index.which | 60, 91 |
| abstract_inverted_index.(MPVF) | 88 |
| abstract_inverted_index.(PVWF) | 113 |
| abstract_inverted_index.First, | 107 |
| abstract_inverted_index.across | 141 |
| abstract_inverted_index.fusion | 33, 43, 87, 112, 124, 133 |
| abstract_inverted_index.global | 52, 128 |
| abstract_inverted_index.inject | 188 |
| abstract_inverted_index.method | 256 |
| abstract_inverted_index.module | 114, 126, 181 |
| abstract_inverted_index.object | 1, 65, 80, 145 |
| abstract_inverted_index.placed | 204 |
| abstract_inverted_index.stage, | 208 |
| abstract_inverted_index.suffer | 45 |
| abstract_inverted_index.(IRFP), | 166 |
| abstract_inverted_index.48.61%, | 249 |
| abstract_inverted_index.69.24%, | 234 |
| abstract_inverted_index.70.12%, | 251 |
| abstract_inverted_index.85.12%, | 248 |
| abstract_inverted_index.Average | 230 |
| abstract_inverted_index.Second, | 155 |
| abstract_inverted_index.address | 74 |
| abstract_inverted_index.between | 49, 139 |
| abstract_inverted_index.combine | 118 |
| abstract_inverted_index.complex | 14, 71, 153 |
| abstract_inverted_index.cyclist | 261 |
| abstract_inverted_index.dataset | 226 |
| abstract_inverted_index.enhance | 36 |
| abstract_inverted_index.feature | 57, 94, 98, 158, 164, 175 |
| abstract_inverted_index.improve | 102 |
| abstract_inverted_index.issues, | 76 |
| abstract_inverted_index.limited | 55 |
| abstract_inverted_index.methods | 20, 44 |
| abstract_inverted_index.module, | 135, 160, 203 |
| abstract_inverted_index.pivotal | 5 |
| abstract_inverted_index.pyramid | 165, 176 |
| abstract_inverted_index.reaches | 247 |
| abstract_inverted_index.results | 61 |
| abstract_inverted_index.scenes. | 72, 154 |
| abstract_inverted_index.traffic | 15 |
| abstract_inverted_index.various | 213 |
| abstract_inverted_index.Finally, | 221 |
| abstract_inverted_index.However, | 39 |
| abstract_inverted_index.accuracy | 241 |
| abstract_inverted_index.accurate | 9 |
| abstract_inverted_index.boosting | 149 |
| abstract_inverted_index.captures | 209 |
| abstract_inverted_index.cyclists | 246 |
| abstract_inverted_index.enhances | 92 |
| abstract_inverted_index.existing | 40 |
| abstract_inverted_index.features | 53, 120, 129, 140, 191, 211 |
| abstract_inverted_index.improved | 161, 171 |
| abstract_inverted_index.modules, | 197 |
| abstract_inverted_index.modules. | 178 |
| abstract_inverted_index.proposed | 116, 255 |
| abstract_inverted_index.semantic | 190 |
| abstract_inverted_index.strategy | 186 |
| abstract_inverted_index.unstable | 68 |
| abstract_inverted_index.Detection | 240 |
| abstract_inverted_index.Precision | 231 |
| abstract_inverted_index.achieving | 8 |
| abstract_inverted_index.algorithm | 82 |
| abstract_inverted_index.detection | 2, 19, 37, 66, 81, 103, 146 |
| abstract_inverted_index.enhancing | 136, 215 |
| abstract_inverted_index.excelling | 257 |
| abstract_inverted_index.improving | 143, 198 |
| abstract_inverted_index.model’s | 217 |
| abstract_inverted_index.necessity | 30 |
| abstract_inverted_index.optimizes | 97 |
| abstract_inverted_index.pointwise | 84, 109, 123 |
| abstract_inverted_index.precision | 25, 104, 218 |
| abstract_inverted_index.proposed, | 90 |
| abstract_inverted_index.scenarios | 16 |
| abstract_inverted_index.voxelwise | 86, 111, 132 |
| abstract_inverted_index.ResNet-101 | 162, 172 |
| abstract_inverted_index.approaches | 34 |
| abstract_inverted_index.comprising | 169 |
| abstract_inverted_index.detection. | 262 |
| abstract_inverted_index.developed, | 168 |
| abstract_inverted_index.evaluation | 222 |
| abstract_inverted_index.expressive | 157 |
| abstract_inverted_index.extraction | 58, 99, 159, 199 |
| abstract_inverted_index.high-level | 189 |
| abstract_inverted_index.highlights | 28 |
| abstract_inverted_index.inadequate | 63 |
| abstract_inverted_index.pedestrian | 259 |
| abstract_inverted_index.strategies | 100 |
| abstract_inverted_index.convolution | 185 |
| abstract_inverted_index.efficiency. | 200 |
| abstract_inverted_index.improvement | 237 |
| abstract_inverted_index.interaction | 48, 95, 138 |
| abstract_inverted_index.modalities, | 142 |
| abstract_inverted_index.multi-modal | 32, 78, 93 |
| abstract_inverted_index.perception, | 11 |
| abstract_inverted_index.performance | 69, 151 |
| abstract_inverted_index.robustness. | 106, 220 |
| abstract_inverted_index.camera-LiDAR | 41 |
| abstract_inverted_index.demonstrates | 227 |
| abstract_inverted_index.fine-grained | 56, 210 |
| abstract_inverted_index.insufficient | 47 |
| abstract_inverted_index.intermediate | 42, 207 |
| abstract_inverted_index.particularly | 12 |
| abstract_inverted_index.pedestrians, | 244 |
| abstract_inverted_index.performance. | 38 |
| abstract_inverted_index.resolutions, | 214 |
| abstract_inverted_index.single-modal | 18 |
| abstract_inverted_index.GraphAlign++. | 239 |
| abstract_inverted_index.capabilities, | 59, 147 |
| abstract_inverted_index.environmental | 10 |
| abstract_inverted_index.requirements. | 26 |
| abstract_inverted_index.respectively, | 252 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.07695102 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |