Gap Completion in Point Cloud Scene occluded by Vehicles using SGC-Net Article Swipe
Yu Feng
,
Yiming Xu
,
Yan Xia
,
Claus Brenner
,
Monika Sester
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.08290
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.08290
Recent advances in mobile mapping systems have greatly enhanced the efficiency and convenience of acquiring urban 3D data. These systems utilize LiDAR sensors mounted on vehicles to capture vast cityscapes. However, a significant challenge arises due to occlusions caused by roadside parked vehicles, leading to the loss of scene information, particularly on the roads, sidewalks, curbs, and the lower sections of buildings. In this study, we present a novel approach that leverages deep neural networks to learn a model capable of filling gaps in urban scenes that are obscured by vehicle occlusion. We have developed an innovative technique where we place virtual vehicle models along road boundaries in the gap-free scene and utilize a ray-casting algorithm to create a new scene with occluded gaps. This allows us to generate diverse and realistic urban point cloud scenes with and without vehicle occlusion, surpassing the limitations of real-world training data collection and annotation. Furthermore, we introduce the Scene Gap Completion Network (SGC-Net), an end-to-end model that can generate well-defined shape boundaries and smooth surfaces within occluded gaps. The experiment results reveal that 97.66% of the filled points fall within a range of 5 centimeters relative to the high-density ground truth point cloud scene. These findings underscore the efficacy of our proposed model in gap completion and reconstructing urban scenes affected by vehicle occlusions.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.08290
- https://arxiv.org/pdf/2407.08290
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400611732
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4400611732Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2407.08290Digital Object Identifier
- Title
-
Gap Completion in Point Cloud Scene occluded by Vehicles using SGC-NetWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-11Full publication date if available
- Authors
-
Yu Feng, Yiming Xu, Yan Xia, Claus Brenner, Monika SesterList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.08290Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2407.08290Direct 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/2407.08290Direct OA link when available
- Concepts
-
Net (polyhedron), Point cloud, Cloud computing, Point (geometry), Computer science, Computer vision, Artificial intelligence, Completion (oil and gas wells), Computer graphics (images), Mathematics, Engineering, Geometry, Operating system, Mechanical engineeringTop 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/W4400611732 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2407.08290 |
| ids.doi | https://doi.org/10.48550/arxiv.2407.08290 |
| ids.openalex | https://openalex.org/W4400611732 |
| fwci | 0.0 |
| type | preprint |
| title | Gap Completion in Point Cloud Scene occluded by Vehicles using SGC-Net |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10719 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9898999929428101 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2206 |
| topics[0].subfield.display_name | Computational Mechanics |
| topics[0].display_name | 3D Shape Modeling and Analysis |
| topics[1].id | https://openalex.org/T11164 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9890000224113464 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2305 |
| topics[1].subfield.display_name | Environmental Engineering |
| topics[1].display_name | Remote Sensing and LiDAR Applications |
| topics[2].id | https://openalex.org/T11211 |
| topics[2].field.id | https://openalex.org/fields/19 |
| topics[2].field.display_name | Earth and Planetary Sciences |
| topics[2].score | 0.9643999934196472 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1907 |
| topics[2].subfield.display_name | Geology |
| topics[2].display_name | 3D Surveying and Cultural Heritage |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C14166107 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7617366313934326 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q253829 |
| concepts[0].display_name | Net (polyhedron) |
| concepts[1].id | https://openalex.org/C131979681 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7206874489784241 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1899648 |
| concepts[1].display_name | Point cloud |
| concepts[2].id | https://openalex.org/C79974875 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5888851881027222 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q483639 |
| concepts[2].display_name | Cloud computing |
| concepts[3].id | https://openalex.org/C28719098 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5779811143875122 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q44946 |
| concepts[3].display_name | Point (geometry) |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.47278711199760437 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C31972630 |
| concepts[5].level | 1 |
| concepts[5].score | 0.43687915802001953 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[5].display_name | Computer vision |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.43271970748901367 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C2779538338 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4104396402835846 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2990590 |
| concepts[7].display_name | Completion (oil and gas wells) |
| concepts[8].id | https://openalex.org/C121684516 |
| concepts[8].level | 1 |
| concepts[8].score | 0.32494208216667175 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7600677 |
| concepts[8].display_name | Computer graphics (images) |
| concepts[9].id | https://openalex.org/C33923547 |
| concepts[9].level | 0 |
| concepts[9].score | 0.2126200795173645 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[9].display_name | Mathematics |
| concepts[10].id | https://openalex.org/C127413603 |
| concepts[10].level | 0 |
| concepts[10].score | 0.18812987208366394 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[10].display_name | Engineering |
| concepts[11].id | https://openalex.org/C2524010 |
| concepts[11].level | 1 |
| concepts[11].score | 0.15316146612167358 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[11].display_name | Geometry |
| concepts[12].id | https://openalex.org/C111919701 |
| concepts[12].level | 1 |
| concepts[12].score | 0.10526898503303528 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[12].display_name | Operating system |
| concepts[13].id | https://openalex.org/C78519656 |
| concepts[13].level | 1 |
| concepts[13].score | 0.056932687759399414 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q101333 |
| concepts[13].display_name | Mechanical engineering |
| keywords[0].id | https://openalex.org/keywords/net |
| keywords[0].score | 0.7617366313934326 |
| keywords[0].display_name | Net (polyhedron) |
| keywords[1].id | https://openalex.org/keywords/point-cloud |
| keywords[1].score | 0.7206874489784241 |
| keywords[1].display_name | Point cloud |
| keywords[2].id | https://openalex.org/keywords/cloud-computing |
| keywords[2].score | 0.5888851881027222 |
| keywords[2].display_name | Cloud computing |
| keywords[3].id | https://openalex.org/keywords/point |
| keywords[3].score | 0.5779811143875122 |
| keywords[3].display_name | Point (geometry) |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.47278711199760437 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/computer-vision |
| keywords[5].score | 0.43687915802001953 |
| keywords[5].display_name | Computer vision |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.43271970748901367 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/completion |
| keywords[7].score | 0.4104396402835846 |
| keywords[7].display_name | Completion (oil and gas wells) |
| keywords[8].id | https://openalex.org/keywords/computer-graphics |
| keywords[8].score | 0.32494208216667175 |
| keywords[8].display_name | Computer graphics (images) |
| keywords[9].id | https://openalex.org/keywords/mathematics |
| keywords[9].score | 0.2126200795173645 |
| keywords[9].display_name | Mathematics |
| keywords[10].id | https://openalex.org/keywords/engineering |
| keywords[10].score | 0.18812987208366394 |
| keywords[10].display_name | Engineering |
| keywords[11].id | https://openalex.org/keywords/geometry |
| keywords[11].score | 0.15316146612167358 |
| keywords[11].display_name | Geometry |
| keywords[12].id | https://openalex.org/keywords/operating-system |
| keywords[12].score | 0.10526898503303528 |
| keywords[12].display_name | Operating system |
| keywords[13].id | https://openalex.org/keywords/mechanical-engineering |
| keywords[13].score | 0.056932687759399414 |
| keywords[13].display_name | Mechanical engineering |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2407.08290 |
| 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/2407.08290 |
| 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/2407.08290 |
| locations[1].id | doi:10.48550/arxiv.2407.08290 |
| 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-journal |
| 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.2407.08290 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5109495151 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Yu Feng |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Feng, Yu |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5058716760 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-2983-1751 |
| authorships[1].author.display_name | Yiming Xu |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Xu, Yiming |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5101917127 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-3843-6275 |
| authorships[2].author.display_name | Yan Xia |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Xia, Yan |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5016827109 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-6459-0682 |
| authorships[3].author.display_name | Claus Brenner |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Brenner, Claus |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5020817045 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-6656-8809 |
| authorships[4].author.display_name | Monika Sester |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Sester, Monika |
| authorships[4].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2407.08290 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Gap Completion in Point Cloud Scene occluded by Vehicles using SGC-Net |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10719 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9898999929428101 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2206 |
| primary_topic.subfield.display_name | Computational Mechanics |
| primary_topic.display_name | 3D Shape Modeling and Analysis |
| related_works | https://openalex.org/W2360737145, https://openalex.org/W4245777471, https://openalex.org/W4233733659, https://openalex.org/W4233380927, https://openalex.org/W4234733573, https://openalex.org/W2362077387, https://openalex.org/W3192151491, https://openalex.org/W4377233624, https://openalex.org/W2351014346, https://openalex.org/W2045577599 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2407.08290 |
| 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/2407.08290 |
| 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/2407.08290 |
| primary_location.id | pmh:oai:arXiv.org:2407.08290 |
| 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/2407.08290 |
| 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/2407.08290 |
| publication_date | 2024-07-11 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.5 | 190 |
| abstract_inverted_index.a | 31, 67, 77, 113, 118, 187 |
| abstract_inverted_index.3D | 16 |
| abstract_inverted_index.In | 62 |
| abstract_inverted_index.We | 92 |
| abstract_inverted_index.an | 95, 160 |
| abstract_inverted_index.by | 39, 89, 218 |
| abstract_inverted_index.in | 2, 83, 107, 210 |
| abstract_inverted_index.of | 13, 47, 60, 80, 144, 181, 189, 206 |
| abstract_inverted_index.on | 24, 51 |
| abstract_inverted_index.to | 26, 36, 44, 75, 116, 127, 193 |
| abstract_inverted_index.us | 126 |
| abstract_inverted_index.we | 65, 99, 152 |
| abstract_inverted_index.Gap | 156 |
| abstract_inverted_index.The | 175 |
| abstract_inverted_index.and | 11, 56, 111, 130, 137, 149, 169, 213 |
| abstract_inverted_index.are | 87 |
| abstract_inverted_index.can | 164 |
| abstract_inverted_index.due | 35 |
| abstract_inverted_index.gap | 211 |
| abstract_inverted_index.new | 119 |
| abstract_inverted_index.our | 207 |
| abstract_inverted_index.the | 9, 45, 52, 57, 108, 142, 154, 182, 194, 204 |
| abstract_inverted_index.This | 124 |
| abstract_inverted_index.data | 147 |
| abstract_inverted_index.deep | 72 |
| abstract_inverted_index.fall | 185 |
| abstract_inverted_index.gaps | 82 |
| abstract_inverted_index.have | 6, 93 |
| abstract_inverted_index.loss | 46 |
| abstract_inverted_index.road | 105 |
| abstract_inverted_index.that | 70, 86, 163, 179 |
| abstract_inverted_index.this | 63 |
| abstract_inverted_index.vast | 28 |
| abstract_inverted_index.with | 121, 136 |
| abstract_inverted_index.LiDAR | 21 |
| abstract_inverted_index.Scene | 155 |
| abstract_inverted_index.These | 18, 201 |
| abstract_inverted_index.along | 104 |
| abstract_inverted_index.cloud | 134, 199 |
| abstract_inverted_index.data. | 17 |
| abstract_inverted_index.gaps. | 123, 174 |
| abstract_inverted_index.learn | 76 |
| abstract_inverted_index.lower | 58 |
| abstract_inverted_index.model | 78, 162, 209 |
| abstract_inverted_index.novel | 68 |
| abstract_inverted_index.place | 100 |
| abstract_inverted_index.point | 133, 198 |
| abstract_inverted_index.range | 188 |
| abstract_inverted_index.scene | 48, 110, 120 |
| abstract_inverted_index.shape | 167 |
| abstract_inverted_index.truth | 197 |
| abstract_inverted_index.urban | 15, 84, 132, 215 |
| abstract_inverted_index.where | 98 |
| abstract_inverted_index.97.66% | 180 |
| abstract_inverted_index.Recent | 0 |
| abstract_inverted_index.allows | 125 |
| abstract_inverted_index.arises | 34 |
| abstract_inverted_index.caused | 38 |
| abstract_inverted_index.create | 117 |
| abstract_inverted_index.curbs, | 55 |
| abstract_inverted_index.filled | 183 |
| abstract_inverted_index.ground | 196 |
| abstract_inverted_index.mobile | 3 |
| abstract_inverted_index.models | 103 |
| abstract_inverted_index.neural | 73 |
| abstract_inverted_index.parked | 41 |
| abstract_inverted_index.points | 184 |
| abstract_inverted_index.reveal | 178 |
| abstract_inverted_index.roads, | 53 |
| abstract_inverted_index.scene. | 200 |
| abstract_inverted_index.scenes | 85, 135, 216 |
| abstract_inverted_index.smooth | 170 |
| abstract_inverted_index.study, | 64 |
| abstract_inverted_index.within | 172, 186 |
| abstract_inverted_index.Network | 158 |
| abstract_inverted_index.capable | 79 |
| abstract_inverted_index.capture | 27 |
| abstract_inverted_index.diverse | 129 |
| abstract_inverted_index.filling | 81 |
| abstract_inverted_index.greatly | 7 |
| abstract_inverted_index.leading | 43 |
| abstract_inverted_index.mapping | 4 |
| abstract_inverted_index.mounted | 23 |
| abstract_inverted_index.present | 66 |
| abstract_inverted_index.results | 177 |
| abstract_inverted_index.sensors | 22 |
| abstract_inverted_index.systems | 5, 19 |
| abstract_inverted_index.utilize | 20, 112 |
| abstract_inverted_index.vehicle | 90, 102, 139, 219 |
| abstract_inverted_index.virtual | 101 |
| abstract_inverted_index.without | 138 |
| abstract_inverted_index.However, | 30 |
| abstract_inverted_index.advances | 1 |
| abstract_inverted_index.affected | 217 |
| abstract_inverted_index.approach | 69 |
| abstract_inverted_index.efficacy | 205 |
| abstract_inverted_index.enhanced | 8 |
| abstract_inverted_index.findings | 202 |
| abstract_inverted_index.gap-free | 109 |
| abstract_inverted_index.generate | 128, 165 |
| abstract_inverted_index.networks | 74 |
| abstract_inverted_index.obscured | 88 |
| abstract_inverted_index.occluded | 122, 173 |
| abstract_inverted_index.proposed | 208 |
| abstract_inverted_index.relative | 192 |
| abstract_inverted_index.roadside | 40 |
| abstract_inverted_index.sections | 59 |
| abstract_inverted_index.surfaces | 171 |
| abstract_inverted_index.training | 146 |
| abstract_inverted_index.vehicles | 25 |
| abstract_inverted_index.acquiring | 14 |
| abstract_inverted_index.algorithm | 115 |
| abstract_inverted_index.challenge | 33 |
| abstract_inverted_index.developed | 94 |
| abstract_inverted_index.introduce | 153 |
| abstract_inverted_index.leverages | 71 |
| abstract_inverted_index.realistic | 131 |
| abstract_inverted_index.technique | 97 |
| abstract_inverted_index.vehicles, | 42 |
| abstract_inverted_index.(SGC-Net), | 159 |
| abstract_inverted_index.Completion | 157 |
| abstract_inverted_index.boundaries | 106, 168 |
| abstract_inverted_index.buildings. | 61 |
| abstract_inverted_index.collection | 148 |
| abstract_inverted_index.completion | 212 |
| abstract_inverted_index.efficiency | 10 |
| abstract_inverted_index.end-to-end | 161 |
| abstract_inverted_index.experiment | 176 |
| abstract_inverted_index.innovative | 96 |
| abstract_inverted_index.occlusion, | 140 |
| abstract_inverted_index.occlusion. | 91 |
| abstract_inverted_index.occlusions | 37 |
| abstract_inverted_index.real-world | 145 |
| abstract_inverted_index.sidewalks, | 54 |
| abstract_inverted_index.surpassing | 141 |
| abstract_inverted_index.underscore | 203 |
| abstract_inverted_index.annotation. | 150 |
| abstract_inverted_index.centimeters | 191 |
| abstract_inverted_index.cityscapes. | 29 |
| abstract_inverted_index.convenience | 12 |
| abstract_inverted_index.limitations | 143 |
| abstract_inverted_index.occlusions. | 220 |
| abstract_inverted_index.ray-casting | 114 |
| abstract_inverted_index.significant | 32 |
| abstract_inverted_index.Furthermore, | 151 |
| abstract_inverted_index.high-density | 195 |
| abstract_inverted_index.information, | 49 |
| abstract_inverted_index.particularly | 50 |
| abstract_inverted_index.well-defined | 166 |
| abstract_inverted_index.reconstructing | 214 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.15060948 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |