SVQNet: Sparse Voxel-Adjacent Query Network for 4D Spatio-Temporal LiDAR Semantic Segmentation Article Swipe
Xuechao Chen
,
Shuangjie Xu
,
Xiaoyi Zou
,
Tongyi Cao
,
Dit‐Yan Yeung
,
Fang Lü
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.13323
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.13323
LiDAR-based semantic perception tasks are critical yet challenging for autonomous driving. Due to the motion of objects and static/dynamic occlusion, temporal information plays an essential role in reinforcing perception by enhancing and completing single-frame knowledge. Previous approaches either directly stack historical frames to the current frame or build a 4D spatio-temporal neighborhood using KNN, which duplicates computation and hinders realtime performance. Based on our observation that stacking all the historical points would damage performance due to a large amount of redundant and misleading information, we propose the Sparse Voxel-Adjacent Query Network (SVQNet) for 4D LiDAR semantic segmentation. To take full advantage of the historical frames high-efficiently, we shunt the historical points into two groups with reference to the current points. One is the Voxel-Adjacent Neighborhood carrying local enhancing knowledge. The other is the Historical Context completing the global knowledge. Then we propose new modules to select and extract the instructive features from the two groups. Our SVQNet achieves state-of-the-art performance in LiDAR semantic segmentation of the SemanticKITTI benchmark and the nuScenes dataset.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.13323
- https://arxiv.org/pdf/2308.13323
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386228516
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4386228516Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2308.13323Digital Object Identifier
- Title
-
SVQNet: Sparse Voxel-Adjacent Query Network for 4D Spatio-Temporal LiDAR Semantic SegmentationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-25Full publication date if available
- Authors
-
Xuechao Chen, Shuangjie Xu, Xiaoyi Zou, Tongyi Cao, Dit‐Yan Yeung, Fang LüList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.13323Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.13323Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2308.13323Direct OA link when available
- Concepts
-
Computer science, Lidar, Segmentation, Artificial intelligence, Benchmark (surveying), Frame (networking), Computer vision, Context (archaeology), Voxel, Pattern recognition (psychology), Data mining, Cartography, Geography, Archaeology, Remote sensing, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4386228516 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2308.13323 |
| ids.doi | https://doi.org/10.48550/arxiv.2308.13323 |
| ids.openalex | https://openalex.org/W4386228516 |
| fwci | |
| type | preprint |
| title | SVQNet: Sparse Voxel-Adjacent Query Network for 4D Spatio-Temporal LiDAR Semantic Segmentation |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| 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 | 0.9997000098228455 |
| 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 | 0.9972000122070312 |
| 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/T11307 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9939000010490417 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Domain Adaptation and Few-Shot Learning |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8388243913650513 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C51399673 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7529155015945435 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q504027 |
| concepts[1].display_name | Lidar |
| concepts[2].id | https://openalex.org/C89600930 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7156835198402405 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[2].display_name | Segmentation |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5825717449188232 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C185798385 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5504303574562073 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1161707 |
| concepts[4].display_name | Benchmark (surveying) |
| concepts[5].id | https://openalex.org/C126042441 |
| concepts[5].level | 2 |
| concepts[5].score | 0.500190258026123 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1324888 |
| concepts[5].display_name | Frame (networking) |
| concepts[6].id | https://openalex.org/C31972630 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4240607023239136 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[6].display_name | Computer vision |
| concepts[7].id | https://openalex.org/C2779343474 |
| concepts[7].level | 2 |
| concepts[7].score | 0.41442710161209106 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q3109175 |
| concepts[7].display_name | Context (archaeology) |
| concepts[8].id | https://openalex.org/C54170458 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4114454686641693 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q663554 |
| concepts[8].display_name | Voxel |
| concepts[9].id | https://openalex.org/C153180895 |
| concepts[9].level | 2 |
| concepts[9].score | 0.34624940156936646 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[9].display_name | Pattern recognition (psychology) |
| concepts[10].id | https://openalex.org/C124101348 |
| concepts[10].level | 1 |
| concepts[10].score | 0.322131872177124 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[10].display_name | Data mining |
| concepts[11].id | https://openalex.org/C58640448 |
| concepts[11].level | 1 |
| concepts[11].score | 0.1165943443775177 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q42515 |
| concepts[11].display_name | Cartography |
| concepts[12].id | https://openalex.org/C205649164 |
| concepts[12].level | 0 |
| concepts[12].score | 0.08273723721504211 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[12].display_name | Geography |
| concepts[13].id | https://openalex.org/C166957645 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q23498 |
| concepts[13].display_name | Archaeology |
| concepts[14].id | https://openalex.org/C62649853 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[14].display_name | Remote sensing |
| concepts[15].id | https://openalex.org/C76155785 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[15].display_name | Telecommunications |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8388243913650513 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/lidar |
| keywords[1].score | 0.7529155015945435 |
| keywords[1].display_name | Lidar |
| keywords[2].id | https://openalex.org/keywords/segmentation |
| keywords[2].score | 0.7156835198402405 |
| keywords[2].display_name | Segmentation |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.5825717449188232 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/benchmark |
| keywords[4].score | 0.5504303574562073 |
| keywords[4].display_name | Benchmark (surveying) |
| keywords[5].id | https://openalex.org/keywords/frame |
| keywords[5].score | 0.500190258026123 |
| keywords[5].display_name | Frame (networking) |
| keywords[6].id | https://openalex.org/keywords/computer-vision |
| keywords[6].score | 0.4240607023239136 |
| keywords[6].display_name | Computer vision |
| keywords[7].id | https://openalex.org/keywords/context |
| keywords[7].score | 0.41442710161209106 |
| keywords[7].display_name | Context (archaeology) |
| keywords[8].id | https://openalex.org/keywords/voxel |
| keywords[8].score | 0.4114454686641693 |
| keywords[8].display_name | Voxel |
| keywords[9].id | https://openalex.org/keywords/pattern-recognition |
| keywords[9].score | 0.34624940156936646 |
| keywords[9].display_name | Pattern recognition (psychology) |
| keywords[10].id | https://openalex.org/keywords/data-mining |
| keywords[10].score | 0.322131872177124 |
| keywords[10].display_name | Data mining |
| keywords[11].id | https://openalex.org/keywords/cartography |
| keywords[11].score | 0.1165943443775177 |
| keywords[11].display_name | Cartography |
| keywords[12].id | https://openalex.org/keywords/geography |
| keywords[12].score | 0.08273723721504211 |
| keywords[12].display_name | Geography |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2308.13323 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2308.13323 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2308.13323 |
| locations[1].id | doi:10.48550/arxiv.2308.13323 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2308.13323 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5050003106 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-1136-3053 |
| authorships[0].author.display_name | Xuechao Chen |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Chen, Xuechao |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5041668587 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-0150-7068 |
| authorships[1].author.display_name | Shuangjie Xu |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Xu, Shuangjie |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5100932903 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0074-1135 |
| authorships[2].author.display_name | Xiaoyi Zou |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Zou, Xiaoyi |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5058424592 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-2157-1526 |
| authorships[3].author.display_name | Tongyi Cao |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Cao, Tongyi |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5073139380 |
| authorships[4].author.orcid | https://orcid.org/0000-0003-3716-8125 |
| authorships[4].author.display_name | Dit‐Yan Yeung |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Yeung, Dit-Yan |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5048654164 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-3763-2012 |
| authorships[5].author.display_name | Fang Lü |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Fang, Lu |
| authorships[5].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2308.13323 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-08-29T00:00:00 |
| display_name | SVQNet: Sparse Voxel-Adjacent Query Network for 4D Spatio-Temporal LiDAR Semantic Segmentation |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| 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 | 0.9997000098228455 |
| 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/W4319317934, https://openalex.org/W2901265155, https://openalex.org/W2956374172, https://openalex.org/W4281783339, https://openalex.org/W4319837668, https://openalex.org/W4308071650, https://openalex.org/W3188333020, https://openalex.org/W1964041166, https://openalex.org/W4390887692, https://openalex.org/W4210818033 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2308.13323 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2308.13323 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2308.13323 |
| primary_location.id | pmh:oai:arXiv.org:2308.13323 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2308.13323 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2308.13323 |
| publication_date | 2023-08-25 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 48, 76 |
| abstract_inverted_index.4D | 49, 93 |
| abstract_inverted_index.To | 97 |
| abstract_inverted_index.an | 23 |
| abstract_inverted_index.by | 29 |
| abstract_inverted_index.in | 26, 160 |
| abstract_inverted_index.is | 121, 131 |
| abstract_inverted_index.of | 15, 79, 101, 164 |
| abstract_inverted_index.on | 62 |
| abstract_inverted_index.or | 46 |
| abstract_inverted_index.to | 12, 42, 75, 116, 144 |
| abstract_inverted_index.we | 84, 106, 140 |
| abstract_inverted_index.Due | 11 |
| abstract_inverted_index.One | 120 |
| abstract_inverted_index.Our | 155 |
| abstract_inverted_index.The | 129 |
| abstract_inverted_index.all | 67 |
| abstract_inverted_index.and | 17, 31, 57, 81, 146, 168 |
| abstract_inverted_index.are | 4 |
| abstract_inverted_index.due | 74 |
| abstract_inverted_index.for | 8, 92 |
| abstract_inverted_index.new | 142 |
| abstract_inverted_index.our | 63 |
| abstract_inverted_index.the | 13, 43, 68, 86, 102, 108, 117, 122, 132, 136, 148, 152, 165, 169 |
| abstract_inverted_index.two | 112, 153 |
| abstract_inverted_index.yet | 6 |
| abstract_inverted_index.KNN, | 53 |
| abstract_inverted_index.Then | 139 |
| abstract_inverted_index.from | 151 |
| abstract_inverted_index.full | 99 |
| abstract_inverted_index.into | 111 |
| abstract_inverted_index.role | 25 |
| abstract_inverted_index.take | 98 |
| abstract_inverted_index.that | 65 |
| abstract_inverted_index.with | 114 |
| abstract_inverted_index.Based | 61 |
| abstract_inverted_index.LiDAR | 94, 161 |
| abstract_inverted_index.Query | 89 |
| abstract_inverted_index.build | 47 |
| abstract_inverted_index.frame | 45 |
| abstract_inverted_index.large | 77 |
| abstract_inverted_index.local | 126 |
| abstract_inverted_index.other | 130 |
| abstract_inverted_index.plays | 22 |
| abstract_inverted_index.shunt | 107 |
| abstract_inverted_index.stack | 39 |
| abstract_inverted_index.tasks | 3 |
| abstract_inverted_index.using | 52 |
| abstract_inverted_index.which | 54 |
| abstract_inverted_index.would | 71 |
| abstract_inverted_index.SVQNet | 156 |
| abstract_inverted_index.Sparse | 87 |
| abstract_inverted_index.amount | 78 |
| abstract_inverted_index.damage | 72 |
| abstract_inverted_index.either | 37 |
| abstract_inverted_index.frames | 41, 104 |
| abstract_inverted_index.global | 137 |
| abstract_inverted_index.groups | 113 |
| abstract_inverted_index.motion | 14 |
| abstract_inverted_index.points | 70, 110 |
| abstract_inverted_index.select | 145 |
| abstract_inverted_index.Context | 134 |
| abstract_inverted_index.Network | 90 |
| abstract_inverted_index.current | 44, 118 |
| abstract_inverted_index.extract | 147 |
| abstract_inverted_index.groups. | 154 |
| abstract_inverted_index.hinders | 58 |
| abstract_inverted_index.modules | 143 |
| abstract_inverted_index.objects | 16 |
| abstract_inverted_index.points. | 119 |
| abstract_inverted_index.propose | 85, 141 |
| abstract_inverted_index.(SVQNet) | 91 |
| abstract_inverted_index.Previous | 35 |
| abstract_inverted_index.achieves | 157 |
| abstract_inverted_index.carrying | 125 |
| abstract_inverted_index.critical | 5 |
| abstract_inverted_index.dataset. | 171 |
| abstract_inverted_index.directly | 38 |
| abstract_inverted_index.driving. | 10 |
| abstract_inverted_index.features | 150 |
| abstract_inverted_index.nuScenes | 170 |
| abstract_inverted_index.realtime | 59 |
| abstract_inverted_index.semantic | 1, 95, 162 |
| abstract_inverted_index.stacking | 66 |
| abstract_inverted_index.temporal | 20 |
| abstract_inverted_index.advantage | 100 |
| abstract_inverted_index.benchmark | 167 |
| abstract_inverted_index.enhancing | 30, 127 |
| abstract_inverted_index.essential | 24 |
| abstract_inverted_index.redundant | 80 |
| abstract_inverted_index.reference | 115 |
| abstract_inverted_index.Historical | 133 |
| abstract_inverted_index.approaches | 36 |
| abstract_inverted_index.autonomous | 9 |
| abstract_inverted_index.completing | 32, 135 |
| abstract_inverted_index.duplicates | 55 |
| abstract_inverted_index.historical | 40, 69, 103, 109 |
| abstract_inverted_index.knowledge. | 34, 128, 138 |
| abstract_inverted_index.misleading | 82 |
| abstract_inverted_index.occlusion, | 19 |
| abstract_inverted_index.perception | 2, 28 |
| abstract_inverted_index.LiDAR-based | 0 |
| abstract_inverted_index.challenging | 7 |
| abstract_inverted_index.computation | 56 |
| abstract_inverted_index.information | 21 |
| abstract_inverted_index.instructive | 149 |
| abstract_inverted_index.observation | 64 |
| abstract_inverted_index.performance | 73, 159 |
| abstract_inverted_index.reinforcing | 27 |
| abstract_inverted_index.Neighborhood | 124 |
| abstract_inverted_index.information, | 83 |
| abstract_inverted_index.neighborhood | 51 |
| abstract_inverted_index.performance. | 60 |
| abstract_inverted_index.segmentation | 163 |
| abstract_inverted_index.single-frame | 33 |
| abstract_inverted_index.SemanticKITTI | 166 |
| abstract_inverted_index.segmentation. | 96 |
| abstract_inverted_index.Voxel-Adjacent | 88, 123 |
| abstract_inverted_index.static/dynamic | 18 |
| abstract_inverted_index.spatio-temporal | 50 |
| abstract_inverted_index.state-of-the-art | 158 |
| abstract_inverted_index.high-efficiently, | 105 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |