PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection Article Swipe
Shaoshuai Shi
,
Li Jiang
,
Jiajun Deng
,
Zhe Wang
,
Chaoxu Guo
,
Jianping Shi
,
Xiaogang Wang
,
Hongsheng Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s11263-022-01710-9
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s11263-022-01710-9
3D object detection is receiving increasing attention from both industry and academia thanks to its wide applications in various fields. In this paper, we propose Point-Voxel Region-based Convolution Neural Networks (PV-RCNNs) for 3D object detection on point clouds. First, we propose a novel 3D detector, PV-RCNN, which boosts the 3D detection performance by deeply integrating the feature learning of both point-based set abstraction and voxel-based sparse convolution through two novel steps, i.e. , the voxel-to-keypoint scene encoding and the keypoint-to-grid RoI feature abstraction. Second, we propose an advanced framework, PV-RCNN++, for more efficient and accurate 3D object detection. It consists of two major improvements: sectorized proposal-centric sampling for efficiently producing more representative keypoints, and VectorPool aggregation for better aggregating local point features with much less resource consumption. With these two strategies, our PV-RCNN++ is about $$3\times $$ faster than PV-RCNN, while also achieving better performance. The experiments demonstrate that our proposed PV-RCNN++ framework achieves state-of-the-art 3D detection performance on the large-scale and highly-competitive Waymo Open Dataset with 10 FPS inference speed on the detection range of $$150m \times 150m$$ .
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11263-022-01710-9
- https://link.springer.com/content/pdf/10.1007/s11263-022-01710-9.pdf
- OA Status
- hybrid
- Cited By
- 414
- References
- 71
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310078553
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4310078553Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s11263-022-01710-9Digital Object Identifier
- Title
-
PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object DetectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-24Full publication date if available
- Authors
-
Shaoshuai Shi, Li Jiang, Jiajun Deng, Zhe Wang, Chaoxu Guo, Jianping Shi, Xiaogang Wang, Hongsheng LiList of authors in order
- Landing page
-
https://doi.org/10.1007/s11263-022-01710-9Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s11263-022-01710-9.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s11263-022-01710-9.pdfDirect OA link when available
- Concepts
-
Computer science, Artificial intelligence, Object detection, Pattern recognition (psychology), Convolutional neural network, Abstraction, Kernel (algebra), Feature (linguistics), Voxel, Algorithm, Mathematics, Epistemology, Philosophy, Combinatorics, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
414Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 137, 2024: 126, 2023: 115, 2022: 26, 2021: 10Per-year citation counts (last 5 years)
- References (count)
-
71Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4310078553 |
|---|---|
| doi | https://doi.org/10.1007/s11263-022-01710-9 |
| ids.doi | https://doi.org/10.1007/s11263-022-01710-9 |
| ids.openalex | https://openalex.org/W4310078553 |
| fwci | 50.01188766 |
| type | article |
| title | PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection |
| biblio.issue | 2 |
| biblio.volume | 131 |
| biblio.last_page | 551 |
| biblio.first_page | 531 |
| topics[0].id | https://openalex.org/T10036 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Advanced Neural Network Applications |
| topics[1].id | https://openalex.org/T10191 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9991999864578247 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2202 |
| topics[1].subfield.display_name | Aerospace Engineering |
| topics[1].display_name | Robotics and Sensor-Based Localization |
| topics[2].id | https://openalex.org/T10812 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9969000220298767 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Human Pose and Action Recognition |
| is_xpac | False |
| apc_list.value | 2890 |
| apc_list.currency | EUR |
| apc_list.value_usd | 3690 |
| apc_paid.value | 2890 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 3690 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.769070565700531 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.7032561898231506 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C2776151529 |
| concepts[2].level | 3 |
| concepts[2].score | 0.61836838722229 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q3045304 |
| concepts[2].display_name | Object detection |
| concepts[3].id | https://openalex.org/C153180895 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5537770390510559 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[3].display_name | Pattern recognition (psychology) |
| concepts[4].id | https://openalex.org/C81363708 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5227779746055603 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[4].display_name | Convolutional neural network |
| concepts[5].id | https://openalex.org/C124304363 |
| concepts[5].level | 2 |
| concepts[5].score | 0.48442819714546204 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q673661 |
| concepts[5].display_name | Abstraction |
| concepts[6].id | https://openalex.org/C74193536 |
| concepts[6].level | 2 |
| concepts[6].score | 0.476790189743042 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q574844 |
| concepts[6].display_name | Kernel (algebra) |
| concepts[7].id | https://openalex.org/C2776401178 |
| concepts[7].level | 2 |
| concepts[7].score | 0.47321194410324097 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[7].display_name | Feature (linguistics) |
| concepts[8].id | https://openalex.org/C54170458 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4576323330402374 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q663554 |
| concepts[8].display_name | Voxel |
| concepts[9].id | https://openalex.org/C11413529 |
| concepts[9].level | 1 |
| concepts[9].score | 0.37856072187423706 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[9].display_name | Algorithm |
| concepts[10].id | https://openalex.org/C33923547 |
| concepts[10].level | 0 |
| concepts[10].score | 0.13975098729133606 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[10].display_name | Mathematics |
| concepts[11].id | https://openalex.org/C111472728 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q9471 |
| concepts[11].display_name | Epistemology |
| concepts[12].id | https://openalex.org/C138885662 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[12].display_name | Philosophy |
| concepts[13].id | https://openalex.org/C114614502 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q76592 |
| concepts[13].display_name | Combinatorics |
| concepts[14].id | https://openalex.org/C41895202 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[14].display_name | Linguistics |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.769070565700531 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.7032561898231506 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/object-detection |
| keywords[2].score | 0.61836838722229 |
| keywords[2].display_name | Object detection |
| keywords[3].id | https://openalex.org/keywords/pattern-recognition |
| keywords[3].score | 0.5537770390510559 |
| keywords[3].display_name | Pattern recognition (psychology) |
| keywords[4].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[4].score | 0.5227779746055603 |
| keywords[4].display_name | Convolutional neural network |
| keywords[5].id | https://openalex.org/keywords/abstraction |
| keywords[5].score | 0.48442819714546204 |
| keywords[5].display_name | Abstraction |
| keywords[6].id | https://openalex.org/keywords/kernel |
| keywords[6].score | 0.476790189743042 |
| keywords[6].display_name | Kernel (algebra) |
| keywords[7].id | https://openalex.org/keywords/feature |
| keywords[7].score | 0.47321194410324097 |
| keywords[7].display_name | Feature (linguistics) |
| keywords[8].id | https://openalex.org/keywords/voxel |
| keywords[8].score | 0.4576323330402374 |
| keywords[8].display_name | Voxel |
| keywords[9].id | https://openalex.org/keywords/algorithm |
| keywords[9].score | 0.37856072187423706 |
| keywords[9].display_name | Algorithm |
| keywords[10].id | https://openalex.org/keywords/mathematics |
| keywords[10].score | 0.13975098729133606 |
| keywords[10].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1007/s11263-022-01710-9 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S25538012 |
| locations[0].source.issn | 0920-5691, 1573-1405 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0920-5691 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | International Journal of Computer Vision |
| locations[0].source.host_organization | https://openalex.org/P4310319900 |
| locations[0].source.host_organization_name | Springer Science+Business Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| locations[0].source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://link.springer.com/content/pdf/10.1007/s11263-022-01710-9.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | International Journal of Computer Vision |
| locations[0].landing_page_url | https://doi.org/10.1007/s11263-022-01710-9 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5083443210 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-2558-181X |
| authorships[0].author.display_name | Shaoshuai Shi |
| authorships[0].countries | HK |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I177725633 |
| authorships[0].affiliations[0].raw_affiliation_string | The Chinese University of Hong Kong, Hong Kong SAR, China |
| authorships[0].institutions[0].id | https://openalex.org/I177725633 |
| authorships[0].institutions[0].ror | https://ror.org/00t33hh48 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I177725633 |
| authorships[0].institutions[0].country_code | HK |
| authorships[0].institutions[0].display_name | Chinese University of Hong Kong |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Shaoshuai Shi |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | The Chinese University of Hong Kong, Hong Kong SAR, China |
| authorships[1].author.id | https://openalex.org/A5100392353 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7058-6957 |
| authorships[1].author.display_name | Li Jiang |
| authorships[1].countries | DE, HK |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I177725633 |
| authorships[1].affiliations[0].raw_affiliation_string | The Chinese University of Hong Kong, Hong Kong SAR, China |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I4210109712 |
| authorships[1].affiliations[1].raw_affiliation_string | Max Planck Institute for Informatics, Saarbrücken, Germany |
| authorships[1].institutions[0].id | https://openalex.org/I4210109712 |
| authorships[1].institutions[0].ror | https://ror.org/01w19ak89 |
| authorships[1].institutions[0].type | facility |
| authorships[1].institutions[0].lineage | https://openalex.org/I149899117, https://openalex.org/I4210109712 |
| authorships[1].institutions[0].country_code | DE |
| authorships[1].institutions[0].display_name | Max Planck Institute for Informatics |
| authorships[1].institutions[1].id | https://openalex.org/I177725633 |
| authorships[1].institutions[1].ror | https://ror.org/00t33hh48 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I177725633 |
| authorships[1].institutions[1].country_code | HK |
| authorships[1].institutions[1].display_name | Chinese University of Hong Kong |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Li Jiang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Max Planck Institute for Informatics, Saarbrücken, Germany, The Chinese University of Hong Kong, Hong Kong SAR, China |
| authorships[2].author.id | https://openalex.org/A5089561793 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-9624-7451 |
| authorships[2].author.display_name | Jiajun Deng |
| authorships[2].countries | AU |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I129604602 |
| authorships[2].affiliations[0].raw_affiliation_string | The University of Sydney, Sydney, Australia |
| authorships[2].institutions[0].id | https://openalex.org/I129604602 |
| authorships[2].institutions[0].ror | https://ror.org/0384j8v12 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I129604602 |
| authorships[2].institutions[0].country_code | AU |
| authorships[2].institutions[0].display_name | The University of Sydney |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Jiajun Deng |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | The University of Sydney, Sydney, Australia |
| authorships[3].author.id | https://openalex.org/A5100407596 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-0597-4475 |
| authorships[3].author.display_name | Zhe Wang |
| authorships[3].countries | HK |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4210128910 |
| authorships[3].affiliations[0].raw_affiliation_string | SenseTime Research, Shanghai, China |
| authorships[3].institutions[0].id | https://openalex.org/I4210128910 |
| authorships[3].institutions[0].ror | https://ror.org/036wd5777 |
| authorships[3].institutions[0].type | company |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210128910 |
| authorships[3].institutions[0].country_code | HK |
| authorships[3].institutions[0].display_name | Group Sense (China) |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Zhe Wang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | SenseTime Research, Shanghai, China |
| authorships[4].author.id | https://openalex.org/A5101981769 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-8715-4827 |
| authorships[4].author.display_name | Chaoxu Guo |
| authorships[4].countries | HK |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4210128910 |
| authorships[4].affiliations[0].raw_affiliation_string | SenseTime Research, Shanghai, China |
| authorships[4].institutions[0].id | https://openalex.org/I4210128910 |
| authorships[4].institutions[0].ror | https://ror.org/036wd5777 |
| authorships[4].institutions[0].type | company |
| authorships[4].institutions[0].lineage | https://openalex.org/I4210128910 |
| authorships[4].institutions[0].country_code | HK |
| authorships[4].institutions[0].display_name | Group Sense (China) |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Chaoxu Guo |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | SenseTime Research, Shanghai, China |
| authorships[5].author.id | https://openalex.org/A5103125510 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-3257-8272 |
| authorships[5].author.display_name | Jianping Shi |
| authorships[5].countries | HK |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I4210128910 |
| authorships[5].affiliations[0].raw_affiliation_string | SenseTime Research, Shanghai, China |
| authorships[5].institutions[0].id | https://openalex.org/I4210128910 |
| authorships[5].institutions[0].ror | https://ror.org/036wd5777 |
| authorships[5].institutions[0].type | company |
| authorships[5].institutions[0].lineage | https://openalex.org/I4210128910 |
| authorships[5].institutions[0].country_code | HK |
| authorships[5].institutions[0].display_name | Group Sense (China) |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Jianping Shi |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | SenseTime Research, Shanghai, China |
| authorships[6].author.id | https://openalex.org/A5100444820 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-7929-5889 |
| authorships[6].author.display_name | Xiaogang Wang |
| authorships[6].countries | HK |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I177725633 |
| authorships[6].affiliations[0].raw_affiliation_string | The Chinese University of Hong Kong, Hong Kong SAR, China |
| authorships[6].institutions[0].id | https://openalex.org/I177725633 |
| authorships[6].institutions[0].ror | https://ror.org/00t33hh48 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I177725633 |
| authorships[6].institutions[0].country_code | HK |
| authorships[6].institutions[0].display_name | Chinese University of Hong Kong |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Xiaogang Wang |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | The Chinese University of Hong Kong, Hong Kong SAR, China |
| authorships[7].author.id | https://openalex.org/A5100732450 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-2664-7975 |
| authorships[7].author.display_name | Hongsheng Li |
| authorships[7].countries | HK |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I177725633 |
| authorships[7].affiliations[0].raw_affiliation_string | The Chinese University of Hong Kong, Hong Kong SAR, China |
| authorships[7].institutions[0].id | https://openalex.org/I177725633 |
| authorships[7].institutions[0].ror | https://ror.org/00t33hh48 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I177725633 |
| authorships[7].institutions[0].country_code | HK |
| authorships[7].institutions[0].display_name | Chinese University of Hong Kong |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Hongsheng Li |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | The Chinese University of Hong Kong, Hong Kong SAR, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://link.springer.com/content/pdf/10.1007/s11263-022-01710-9.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10036 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Advanced Neural Network Applications |
| related_works | https://openalex.org/W3027020613, https://openalex.org/W2016533837, https://openalex.org/W3167885074, https://openalex.org/W2892386716, https://openalex.org/W1998563493, https://openalex.org/W4306164210, https://openalex.org/W4313316311, https://openalex.org/W4362608745, https://openalex.org/W2969228573, https://openalex.org/W2963690996 |
| cited_by_count | 414 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 137 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 126 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 115 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 26 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 10 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1007/s11263-022-01710-9 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S25538012 |
| best_oa_location.source.issn | 0920-5691, 1573-1405 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0920-5691 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | International Journal of Computer Vision |
| best_oa_location.source.host_organization | https://openalex.org/P4310319900 |
| best_oa_location.source.host_organization_name | Springer Science+Business Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| best_oa_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s11263-022-01710-9.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | International Journal of Computer Vision |
| best_oa_location.landing_page_url | https://doi.org/10.1007/s11263-022-01710-9 |
| primary_location.id | doi:10.1007/s11263-022-01710-9 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S25538012 |
| primary_location.source.issn | 0920-5691, 1573-1405 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0920-5691 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | International Journal of Computer Vision |
| primary_location.source.host_organization | https://openalex.org/P4310319900 |
| primary_location.source.host_organization_name | Springer Science+Business Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s11263-022-01710-9.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | International Journal of Computer Vision |
| primary_location.landing_page_url | https://doi.org/10.1007/s11263-022-01710-9 |
| publication_date | 2022-11-24 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2999947750, https://openalex.org/W2605189827, https://openalex.org/W3109675406, https://openalex.org/W2468368736, https://openalex.org/W2555618208, https://openalex.org/W3003618643, https://openalex.org/W3034407526, https://openalex.org/W2963125977, https://openalex.org/W2150066425, https://openalex.org/W1536680647, https://openalex.org/W2963182550, https://openalex.org/W6601977772, https://openalex.org/W2963517242, https://openalex.org/W3108426750, https://openalex.org/W3010797203, https://openalex.org/W2991084087, https://openalex.org/W6601473709, https://openalex.org/W2963400571, https://openalex.org/W3004351857, https://openalex.org/W2968296999, https://openalex.org/W2964166085, https://openalex.org/W2962807143, https://openalex.org/W3114509423, https://openalex.org/W4382464460, https://openalex.org/W3167732492, https://openalex.org/W4312894406, https://openalex.org/W3117804044, https://openalex.org/W2951517617, https://openalex.org/W2565639579, https://openalex.org/W2963351448, https://openalex.org/W2193145675, https://openalex.org/W4225793049, https://openalex.org/W4390872833, https://openalex.org/W3109646990, https://openalex.org/W2972211064, https://openalex.org/W4214777292, https://openalex.org/W2560544142, https://openalex.org/W2963667201, https://openalex.org/W3109395584, https://openalex.org/W2964062501, https://openalex.org/W2988715931, https://openalex.org/W3035749845, https://openalex.org/W3173668541, https://openalex.org/W2963037989, https://openalex.org/W3205005447, https://openalex.org/W2949708697, https://openalex.org/W3034314779, https://openalex.org/W3008105217, https://openalex.org/W2229637417, https://openalex.org/W2788158258, https://openalex.org/W3035172746, https://openalex.org/W3175563878, https://openalex.org/W2990613095, https://openalex.org/W3035461736, https://openalex.org/W2954174912, https://openalex.org/W2979750740, https://openalex.org/W3107212734, https://openalex.org/W3004237909, https://openalex.org/W2963231572, https://openalex.org/W2897529137, https://openalex.org/W2798965597, https://openalex.org/W2981949127, https://openalex.org/W3035346742, https://openalex.org/W3171377125, https://openalex.org/W3034236957, https://openalex.org/W3167095230, https://openalex.org/W3017930107, https://openalex.org/W2960986959, https://openalex.org/W2963727135, https://openalex.org/W3107819843, https://openalex.org/W3106250896 |
| referenced_works_count | 71 |
| abstract_inverted_index., | 73 |
| abstract_inverted_index.. | 184 |
| abstract_inverted_index.a | 42 |
| abstract_inverted_index.$$ | 137 |
| abstract_inverted_index.10 | 170 |
| abstract_inverted_index.3D | 1, 33, 44, 50, 96, 158 |
| abstract_inverted_index.In | 21 |
| abstract_inverted_index.It | 99 |
| abstract_inverted_index.an | 87 |
| abstract_inverted_index.by | 53 |
| abstract_inverted_index.in | 18 |
| abstract_inverted_index.is | 4, 134 |
| abstract_inverted_index.of | 59, 101, 178 |
| abstract_inverted_index.on | 36, 161, 174 |
| abstract_inverted_index.to | 14 |
| abstract_inverted_index.we | 24, 40, 85 |
| abstract_inverted_index.FPS | 171 |
| abstract_inverted_index.RoI | 81 |
| abstract_inverted_index.The | 148 |
| abstract_inverted_index.and | 11, 64, 78, 94, 114, 164 |
| abstract_inverted_index.for | 32, 91, 108, 117 |
| abstract_inverted_index.its | 15 |
| abstract_inverted_index.our | 132, 152 |
| abstract_inverted_index.set | 62 |
| abstract_inverted_index.the | 49, 56, 74, 79, 162, 175 |
| abstract_inverted_index.two | 69, 102, 130 |
| abstract_inverted_index.Open | 167 |
| abstract_inverted_index.With | 128 |
| abstract_inverted_index.also | 144 |
| abstract_inverted_index.both | 9, 60 |
| abstract_inverted_index.from | 8 |
| abstract_inverted_index.i.e. | 72 |
| abstract_inverted_index.less | 125 |
| abstract_inverted_index.more | 92, 111 |
| abstract_inverted_index.much | 124 |
| abstract_inverted_index.than | 141 |
| abstract_inverted_index.that | 151 |
| abstract_inverted_index.this | 22 |
| abstract_inverted_index.wide | 16 |
| abstract_inverted_index.with | 123, 169 |
| abstract_inverted_index.Waymo | 166 |
| abstract_inverted_index.about | 135 |
| abstract_inverted_index.local | 120 |
| abstract_inverted_index.major | 103 |
| abstract_inverted_index.novel | 43, 70 |
| abstract_inverted_index.point | 37, 121 |
| abstract_inverted_index.range | 177 |
| abstract_inverted_index.scene | 76 |
| abstract_inverted_index.speed | 173 |
| abstract_inverted_index.these | 129 |
| abstract_inverted_index.which | 47 |
| abstract_inverted_index.while | 143 |
| abstract_inverted_index.$$150m | 179 |
| abstract_inverted_index.150m$$ | 181 |
| abstract_inverted_index.First, | 39 |
| abstract_inverted_index.Neural | 29 |
| abstract_inverted_index.\times | 180 |
| abstract_inverted_index.better | 118, 146 |
| abstract_inverted_index.boosts | 48 |
| abstract_inverted_index.deeply | 54 |
| abstract_inverted_index.faster | 140 |
| abstract_inverted_index.object | 2, 34, 97 |
| abstract_inverted_index.paper, | 23 |
| abstract_inverted_index.sparse | 66 |
| abstract_inverted_index.steps, | 71 |
| abstract_inverted_index.thanks | 13 |
| abstract_inverted_index.Dataset | 168 |
| abstract_inverted_index.Second, | 84 |
| abstract_inverted_index.clouds. | 38 |
| abstract_inverted_index.feature | 57, 82 |
| abstract_inverted_index.fields. | 20 |
| abstract_inverted_index.propose | 25, 41, 86 |
| abstract_inverted_index.through | 68 |
| abstract_inverted_index.various | 19 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Networks | 30 |
| abstract_inverted_index.PV-RCNN, | 46, 142 |
| abstract_inverted_index.academia | 12 |
| abstract_inverted_index.accurate | 95 |
| abstract_inverted_index.achieves | 156 |
| abstract_inverted_index.advanced | 88 |
| abstract_inverted_index.consists | 100 |
| abstract_inverted_index.encoding | 77 |
| abstract_inverted_index.features | 122 |
| abstract_inverted_index.industry | 10 |
| abstract_inverted_index.learning | 58 |
| abstract_inverted_index.proposed | 153 |
| abstract_inverted_index.resource | 126 |
| abstract_inverted_index.sampling | 107 |
| abstract_inverted_index.$$3\times | 136 |
| abstract_inverted_index.<mml:math | 138, 182 |
| abstract_inverted_index.PV-RCNN++ | 133, 154 |
| abstract_inverted_index.achieving | 145 |
| abstract_inverted_index.attention | 7 |
| abstract_inverted_index.detection | 3, 35, 51, 159, 176 |
| abstract_inverted_index.detector, | 45 |
| abstract_inverted_index.efficient | 93 |
| abstract_inverted_index.framework | 155 |
| abstract_inverted_index.inference | 172 |
| abstract_inverted_index.producing | 110 |
| abstract_inverted_index.receiving | 5 |
| abstract_inverted_index.(PV-RCNNs) | 31 |
| abstract_inverted_index.PV-RCNN++, | 90 |
| abstract_inverted_index.VectorPool | 115 |
| abstract_inverted_index.detection. | 98 |
| abstract_inverted_index.framework, | 89 |
| abstract_inverted_index.increasing | 6 |
| abstract_inverted_index.keypoints, | 113 |
| abstract_inverted_index.sectorized | 105 |
| abstract_inverted_index.Convolution | 28 |
| abstract_inverted_index.Point-Voxel | 26 |
| abstract_inverted_index.abstraction | 63 |
| abstract_inverted_index.aggregating | 119 |
| abstract_inverted_index.aggregation | 116 |
| abstract_inverted_index.convolution | 67 |
| abstract_inverted_index.demonstrate | 150 |
| abstract_inverted_index.efficiently | 109 |
| abstract_inverted_index.experiments | 149 |
| abstract_inverted_index.integrating | 55 |
| abstract_inverted_index.large-scale | 163 |
| abstract_inverted_index.performance | 52, 160 |
| abstract_inverted_index.point-based | 61 |
| abstract_inverted_index.strategies, | 131 |
| abstract_inverted_index.voxel-based | 65 |
| abstract_inverted_index.Region-based | 27 |
| abstract_inverted_index.abstraction. | 83 |
| abstract_inverted_index.applications | 17 |
| abstract_inverted_index.consumption. | 127 |
| abstract_inverted_index.performance. | 147 |
| abstract_inverted_index.improvements: | 104 |
| abstract_inverted_index.representative | 112 |
| abstract_inverted_index.keypoint-to-grid | 80 |
| abstract_inverted_index.proposal-centric | 106 |
| abstract_inverted_index.state-of-the-art | 157 |
| abstract_inverted_index.voxel-to-keypoint | 75 |
| abstract_inverted_index.highly-competitive | 165 |
| abstract_inverted_index.xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>3</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math> | 139 |
| abstract_inverted_index.xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>150</mml:mn><mml:mi>m</mml:mi><mml:mo>×</mml:mo><mml:mn>150</mml:mn><mml:mi>m</mml:mi></mml:mrow></mml:math> | 183 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5083443210 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I177725633 |
| citation_normalized_percentile.value | 0.99912456 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |