PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes
Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pos...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zou, Xianghong [verfasserIn] Li, Jianping [verfasserIn] Wang, Yuan [verfasserIn] Liang, Fuxun [verfasserIn] Wu, Weitong [verfasserIn] Wang, Haiping [verfasserIn] Yang, Bisheng [verfasserIn] Dong, Zhen [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: ISPRS journal of photogrammetry and remote sensing - International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7, Amsterdam [u.a.] : Elsevier, 1989, 206, Seite 273-292 |
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| Übergeordnetes Werk: |
volume:206 ; pages:273-292 |
| DOI / URN: |
10.1016/j.isprsjprs.2023.11.005 |
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| Katalog-ID: |
ELV065966996 |
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| 245 | 1 | 0 | |a PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes |
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| 520 | |a Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recalltop1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet. | ||
| 650 | 4 | |a Point cloud | |
| 650 | 4 | |a Place recognition | |
| 650 | 4 | |a Localization | |
| 650 | 4 | |a Heterogeneous | |
| 650 | 4 | |a Patch feature augmentation | |
| 700 | 1 | |a Li, Jianping |e verfasserin |0 (orcid)0000-0003-4813-5126 |4 aut | |
| 700 | 1 | |a Wang, Yuan |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Fuxun |e verfasserin |0 (orcid)0000-0002-5947-4732 |4 aut | |
| 700 | 1 | |a Wu, Weitong |e verfasserin |0 (orcid)0000-0001-8644-534X |4 aut | |
| 700 | 1 | |a Wang, Haiping |e verfasserin |0 (orcid)0000-0002-8370-4585 |4 aut | |
| 700 | 1 | |a Yang, Bisheng |e verfasserin |0 (orcid)0000-0001-7736-0803 |4 aut | |
| 700 | 1 | |a Dong, Zhen |e verfasserin |0 (orcid)0000-0002-0152-3300 |4 aut | |
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10.1016/j.isprsjprs.2023.11.005 doi (DE-627)ELV065966996 (ELSEVIER)S0924-2716(23)00310-6 DE-627 ger DE-627 rda eng 550 VZ 38.73 bkl 74.41 bkl Zou, Xianghong verfasserin (orcid)0000-0002-3110-6153 aut PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recalltop1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet. Point cloud Place recognition Localization Heterogeneous Patch feature augmentation Li, Jianping verfasserin (orcid)0000-0003-4813-5126 aut Wang, Yuan verfasserin aut Liang, Fuxun verfasserin (orcid)0000-0002-5947-4732 aut Wu, Weitong verfasserin (orcid)0000-0001-8644-534X aut Wang, Haiping verfasserin (orcid)0000-0002-8370-4585 aut Yang, Bisheng verfasserin (orcid)0000-0001-7736-0803 aut Dong, Zhen verfasserin (orcid)0000-0002-0152-3300 aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 206, Seite 273-292 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:206 pages:273-292 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.73 Geodäsie VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 206 273-292 |
| spelling |
10.1016/j.isprsjprs.2023.11.005 doi (DE-627)ELV065966996 (ELSEVIER)S0924-2716(23)00310-6 DE-627 ger DE-627 rda eng 550 VZ 38.73 bkl 74.41 bkl Zou, Xianghong verfasserin (orcid)0000-0002-3110-6153 aut PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recalltop1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet. Point cloud Place recognition Localization Heterogeneous Patch feature augmentation Li, Jianping verfasserin (orcid)0000-0003-4813-5126 aut Wang, Yuan verfasserin aut Liang, Fuxun verfasserin (orcid)0000-0002-5947-4732 aut Wu, Weitong verfasserin (orcid)0000-0001-8644-534X aut Wang, Haiping verfasserin (orcid)0000-0002-8370-4585 aut Yang, Bisheng verfasserin (orcid)0000-0001-7736-0803 aut Dong, Zhen verfasserin (orcid)0000-0002-0152-3300 aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 206, Seite 273-292 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:206 pages:273-292 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.73 Geodäsie VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 206 273-292 |
| allfields_unstemmed |
10.1016/j.isprsjprs.2023.11.005 doi (DE-627)ELV065966996 (ELSEVIER)S0924-2716(23)00310-6 DE-627 ger DE-627 rda eng 550 VZ 38.73 bkl 74.41 bkl Zou, Xianghong verfasserin (orcid)0000-0002-3110-6153 aut PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recalltop1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet. Point cloud Place recognition Localization Heterogeneous Patch feature augmentation Li, Jianping verfasserin (orcid)0000-0003-4813-5126 aut Wang, Yuan verfasserin aut Liang, Fuxun verfasserin (orcid)0000-0002-5947-4732 aut Wu, Weitong verfasserin (orcid)0000-0001-8644-534X aut Wang, Haiping verfasserin (orcid)0000-0002-8370-4585 aut Yang, Bisheng verfasserin (orcid)0000-0001-7736-0803 aut Dong, Zhen verfasserin (orcid)0000-0002-0152-3300 aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 206, Seite 273-292 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:206 pages:273-292 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.73 Geodäsie VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 206 273-292 |
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10.1016/j.isprsjprs.2023.11.005 doi (DE-627)ELV065966996 (ELSEVIER)S0924-2716(23)00310-6 DE-627 ger DE-627 rda eng 550 VZ 38.73 bkl 74.41 bkl Zou, Xianghong verfasserin (orcid)0000-0002-3110-6153 aut PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recalltop1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet. Point cloud Place recognition Localization Heterogeneous Patch feature augmentation Li, Jianping verfasserin (orcid)0000-0003-4813-5126 aut Wang, Yuan verfasserin aut Liang, Fuxun verfasserin (orcid)0000-0002-5947-4732 aut Wu, Weitong verfasserin (orcid)0000-0001-8644-534X aut Wang, Haiping verfasserin (orcid)0000-0002-8370-4585 aut Yang, Bisheng verfasserin (orcid)0000-0001-7736-0803 aut Dong, Zhen verfasserin (orcid)0000-0002-0152-3300 aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 206, Seite 273-292 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:206 pages:273-292 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.73 Geodäsie VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 206 273-292 |
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10.1016/j.isprsjprs.2023.11.005 doi (DE-627)ELV065966996 (ELSEVIER)S0924-2716(23)00310-6 DE-627 ger DE-627 rda eng 550 VZ 38.73 bkl 74.41 bkl Zou, Xianghong verfasserin (orcid)0000-0002-3110-6153 aut PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recalltop1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet. Point cloud Place recognition Localization Heterogeneous Patch feature augmentation Li, Jianping verfasserin (orcid)0000-0003-4813-5126 aut Wang, Yuan verfasserin aut Liang, Fuxun verfasserin (orcid)0000-0002-5947-4732 aut Wu, Weitong verfasserin (orcid)0000-0001-8644-534X aut Wang, Haiping verfasserin (orcid)0000-0002-8370-4585 aut Yang, Bisheng verfasserin (orcid)0000-0001-7736-0803 aut Dong, Zhen verfasserin (orcid)0000-0002-0152-3300 aut Enthalten in International Society for Photogrammetry and Remote Sensing ; ID: gnd/132008-7 ISPRS journal of photogrammetry and remote sensing Amsterdam [u.a.] : Elsevier, 1989 206, Seite 273-292 Online-Ressource (DE-627)320504557 (DE-600)2012663-3 (DE-576)096806567 0924-2716 nnns volume:206 pages:273-292 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.73 Geodäsie VZ 74.41 Luftaufnahmen Photogrammetrie VZ AR 206 273-292 |
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Point cloud Place recognition Localization Heterogeneous Patch feature augmentation |
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Zou, Xianghong @@aut@@ Li, Jianping @@aut@@ Wang, Yuan @@aut@@ Liang, Fuxun @@aut@@ Wu, Weitong @@aut@@ Wang, Haiping @@aut@@ Yang, Bisheng @@aut@@ Dong, Zhen @@aut@@ |
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2023-01-01T00:00:00Z |
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Zou, Xianghong |
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Zou, Xianghong ddc 550 bkl 38.73 bkl 74.41 misc Point cloud misc Place recognition misc Localization misc Heterogeneous misc Patch feature augmentation PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes |
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550 VZ 38.73 bkl 74.41 bkl PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes Point cloud Place recognition Localization Heterogeneous Patch feature augmentation |
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Zou, Xianghong Li, Jianping Wang, Yuan Liang, Fuxun Wu, Weitong Wang, Haiping Yang, Bisheng Dong, Zhen |
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patchaugnet: patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes |
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PatchAugNet: Patch feature augmentation-based heterogeneous point cloud place recognition in large-scale street scenes |
| abstract |
Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recalltop1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet. |
| abstractGer |
Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recalltop1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet. |
| abstract_unstemmed |
Point Cloud Place Recognition (PCPR) in street scenes is an essential task in the fields of autonomous driving, robot navigation, and urban map updating. However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recalltop1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet. |
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However, the domain gap between heterogeneous point clouds and the difficulty of feature characterization in large-scale complex street scenes pose significant challenges for existing PCPR methods. Most PCPR methods only take into account point clouds collected by the same platforms and sensors, thus they are with poor domain transferability. In this paper, we propose PatchAugNet, which utilizes patch feature augmentation and adaptive pyramid feature aggregation to achieve better performance and generalizability for Heterogeneous Point Cloud-based Place Recognition (HPCPR) tasks. Firstly, multi-scale local features are extracted by the pyramid feature extraction module. Secondly, local features are enhanced by the patch feature augmentation module to overcome the domain gap problem and achieve better feature representation as well as network generalizability. Finally, a global feature is generated using an adaptive pyramid feature aggregation module, which automatically adjusts and balances the proportion of intra-scale and inter-scale features according to the scene content. To evaluate the performance of PatchAugNet, a large-scale heterogeneous point cloud dataset consisting of high-precision Mobile Laser Scanning (MLS) point clouds and helmet-mounted Portable Laser Scanning (PLS) point clouds is collected. The dataset covers various street scenes with a length of over 20 km. The comprehensive experimental results indicate that PatchAugNet achieves State-Of-The-Art (SOTA) performance with 83.43 % recalltop1% and 60.34 % recall@top1 on unseen large-scale street scenes, outperforming existing SOTA PCPR methods by + 9.57 recall@top1% and + 15.50 recall@top1, while exhibiting better generalizability. For source code and detailed experimental results, please refer to: https://github.com/WHU-USI3DV/PatchAugNet.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Point cloud</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Place recognition</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Localization</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Heterogeneous</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Patch feature augmentation</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Jianping</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-4813-5126</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Yuan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liang, Fuxun</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-5947-4732</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Weitong</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-8644-534X</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Haiping</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-8370-4585</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yang, Bisheng</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-7736-0803</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dong, 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| score |
7.402128 |