Camera-view supervision for bird's-eye-view semantic segmentation Article Swipe
Bowen Yang
,
Lequan Yu
,
Chen Feng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fdata.2024.1431346
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fdata.2024.1431346
Bird's-eye-view Semantic Segmentation (BEVSS) is a powerful and crucial component of planning and control systems in many autonomous vehicles. Current methods rely on end-to-end learning to train models, leading to indirectly supervised and inaccurate camera-to-BEV projections. We propose a novel method of supervising feature extraction with camera-view depth and segmentation information, which improves the quality of feature extraction and projection in the BEVSS pipeline. Our model, evaluated on the nuScenes dataset, shows a 3.8% improvement in Intersection-over-Union (IoU) for vehicle segmentation and a 30-fold reduction in depth error compared to baselines, while maintaining competitive inference times of 32 FPS. This method offers more accurate and reliable BEVSS for real-time autonomous driving systems. The codes and implementation details and code can be found at https://github.com/bluffish/sucam .
Related Topics
Concepts
Computer science
Segmentation
Artificial intelligence
Pipeline (software)
Computer vision
Inference
Intersection (aeronautics)
Projection (relational algebra)
Code (set theory)
Feature (linguistics)
Feature extraction
Engineering
Aerospace engineering
Linguistics
Set (abstract data type)
Algorithm
Programming language
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fdata.2024.1431346
- https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1431346/pdf
- OA Status
- gold
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404427595
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4404427595Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fdata.2024.1431346Digital Object Identifier
- Title
-
Camera-view supervision for bird's-eye-view semantic segmentationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-15Full publication date if available
- Authors
-
Bowen Yang, Lequan Yu, Chen FengList of authors in order
- Landing page
-
https://doi.org/10.3389/fdata.2024.1431346Publisher landing page
- PDF URL
-
https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1431346/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1431346/pdfDirect OA link when available
- Concepts
-
Computer science, Segmentation, Artificial intelligence, Pipeline (software), Computer vision, Inference, Intersection (aeronautics), Projection (relational algebra), Code (set theory), Feature (linguistics), Feature extraction, Engineering, Aerospace engineering, Linguistics, Set (abstract data type), Algorithm, Programming language, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4404427595 |
|---|---|
| doi | https://doi.org/10.3389/fdata.2024.1431346 |
| ids.doi | https://doi.org/10.3389/fdata.2024.1431346 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39618507 |
| ids.openalex | https://openalex.org/W4404427595 |
| fwci | 0.0 |
| type | article |
| title | Camera-view supervision for bird's-eye-view semantic segmentation |
| biblio.issue | |
| biblio.volume | 7 |
| biblio.last_page | 1431346 |
| biblio.first_page | 1431346 |
| topics[0].id | https://openalex.org/T10531 |
| 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 Vision and Imaging |
| topics[1].id | https://openalex.org/T10036 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9998000264167786 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1707 |
| topics[1].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[1].display_name | Advanced Neural Network Applications |
| topics[2].id | https://openalex.org/T10331 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9995999932289124 |
| 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 | Video Surveillance and Tracking Methods |
| is_xpac | False |
| apc_list.value | 1150 |
| apc_list.currency | USD |
| apc_list.value_usd | 1150 |
| apc_paid.value | 1150 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 1150 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.8198057413101196 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C89600930 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7644977569580078 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1423946 |
| concepts[1].display_name | Segmentation |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.7447315454483032 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C43521106 |
| concepts[3].level | 2 |
| concepts[3].score | 0.714928388595581 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2165493 |
| concepts[3].display_name | Pipeline (software) |
| concepts[4].id | https://openalex.org/C31972630 |
| concepts[4].level | 1 |
| concepts[4].score | 0.6553444266319275 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[4].display_name | Computer vision |
| concepts[5].id | https://openalex.org/C2776214188 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5971208810806274 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[5].display_name | Inference |
| concepts[6].id | https://openalex.org/C64543145 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5728676915168762 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q162942 |
| concepts[6].display_name | Intersection (aeronautics) |
| concepts[7].id | https://openalex.org/C57493831 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5354006290435791 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q3134666 |
| concepts[7].display_name | Projection (relational algebra) |
| concepts[8].id | https://openalex.org/C2776760102 |
| concepts[8].level | 3 |
| concepts[8].score | 0.5131857991218567 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q5139990 |
| concepts[8].display_name | Code (set theory) |
| concepts[9].id | https://openalex.org/C2776401178 |
| concepts[9].level | 2 |
| concepts[9].score | 0.47876372933387756 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[9].display_name | Feature (linguistics) |
| concepts[10].id | https://openalex.org/C52622490 |
| concepts[10].level | 2 |
| concepts[10].score | 0.46525484323501587 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1026626 |
| concepts[10].display_name | Feature extraction |
| concepts[11].id | https://openalex.org/C127413603 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[11].display_name | Engineering |
| concepts[12].id | https://openalex.org/C146978453 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q3798668 |
| concepts[12].display_name | Aerospace engineering |
| concepts[13].id | https://openalex.org/C41895202 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[13].display_name | Linguistics |
| concepts[14].id | https://openalex.org/C177264268 |
| concepts[14].level | 2 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[14].display_name | Set (abstract data type) |
| concepts[15].id | https://openalex.org/C11413529 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[15].display_name | Algorithm |
| concepts[16].id | https://openalex.org/C199360897 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[16].display_name | Programming language |
| concepts[17].id | https://openalex.org/C138885662 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[17].display_name | Philosophy |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.8198057413101196 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/segmentation |
| keywords[1].score | 0.7644977569580078 |
| keywords[1].display_name | Segmentation |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.7447315454483032 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/pipeline |
| keywords[3].score | 0.714928388595581 |
| keywords[3].display_name | Pipeline (software) |
| keywords[4].id | https://openalex.org/keywords/computer-vision |
| keywords[4].score | 0.6553444266319275 |
| keywords[4].display_name | Computer vision |
| keywords[5].id | https://openalex.org/keywords/inference |
| keywords[5].score | 0.5971208810806274 |
| keywords[5].display_name | Inference |
| keywords[6].id | https://openalex.org/keywords/intersection |
| keywords[6].score | 0.5728676915168762 |
| keywords[6].display_name | Intersection (aeronautics) |
| keywords[7].id | https://openalex.org/keywords/projection |
| keywords[7].score | 0.5354006290435791 |
| keywords[7].display_name | Projection (relational algebra) |
| keywords[8].id | https://openalex.org/keywords/code |
| keywords[8].score | 0.5131857991218567 |
| keywords[8].display_name | Code (set theory) |
| keywords[9].id | https://openalex.org/keywords/feature |
| keywords[9].score | 0.47876372933387756 |
| keywords[9].display_name | Feature (linguistics) |
| keywords[10].id | https://openalex.org/keywords/feature-extraction |
| keywords[10].score | 0.46525484323501587 |
| keywords[10].display_name | Feature extraction |
| language | en |
| locations[0].id | doi:10.3389/fdata.2024.1431346 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210201220 |
| locations[0].source.issn | 2624-909X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2624-909X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Frontiers in Big Data |
| locations[0].source.host_organization | https://openalex.org/P4310320527 |
| locations[0].source.host_organization_name | Frontiers Media |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320527 |
| locations[0].source.host_organization_lineage_names | Frontiers Media |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1431346/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 | Frontiers in Big Data |
| locations[0].landing_page_url | https://doi.org/10.3389/fdata.2024.1431346 |
| locations[1].id | pmid:39618507 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Frontiers in big data |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39618507 |
| locations[2].id | pmh:oai:doaj.org/article:89969f45ed6a461cb2a77afc43d7b2cd |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Frontiers in Big Data, Vol 7 (2024) |
| locations[2].landing_page_url | https://doaj.org/article/89969f45ed6a461cb2a77afc43d7b2cd |
| locations[3].id | pmh:oai:pubmedcentral.nih.gov:11604745 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S2764455111 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | PubMed Central |
| locations[3].source.host_organization | https://openalex.org/I1299303238 |
| locations[3].source.host_organization_name | National Institutes of Health |
| locations[3].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[3].license | other-oa |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/other-oa |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | Front Big Data |
| locations[3].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/11604745 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5028766722 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6305-5565 |
| authorships[0].author.display_name | Bowen Yang |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I162577319 |
| authorships[0].affiliations[0].raw_affiliation_string | AI Safety Laboratory, Department of Computer Science, The University of Texas at Dallas, Richardson, TX, United States |
| authorships[0].institutions[0].id | https://openalex.org/I162577319 |
| authorships[0].institutions[0].ror | https://ror.org/049emcs32 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I162577319 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | The University of Texas at Dallas |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Bowen Yang |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | AI Safety Laboratory, Department of Computer Science, The University of Texas at Dallas, Richardson, TX, United States |
| authorships[1].author.id | https://openalex.org/A5012581106 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-9315-6527 |
| authorships[1].author.display_name | Lequan Yu |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I162577319 |
| authorships[1].affiliations[0].raw_affiliation_string | AI Safety Laboratory, Department of Computer Science, The University of Texas at Dallas, Richardson, TX, United States |
| authorships[1].institutions[0].id | https://openalex.org/I162577319 |
| authorships[1].institutions[0].ror | https://ror.org/049emcs32 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I162577319 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | The University of Texas at Dallas |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | LinLin Yu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | AI Safety Laboratory, Department of Computer Science, The University of Texas at Dallas, Richardson, TX, United States |
| authorships[2].author.id | https://openalex.org/A5100699149 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-6726-0259 |
| authorships[2].author.display_name | Chen Feng |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I162577319 |
| authorships[2].affiliations[0].raw_affiliation_string | AI Safety Laboratory, Department of Computer Science, The University of Texas at Dallas, Richardson, TX, United States |
| authorships[2].institutions[0].id | https://openalex.org/I162577319 |
| authorships[2].institutions[0].ror | https://ror.org/049emcs32 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I162577319 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | The University of Texas at Dallas |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Feng Chen |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | AI Safety Laboratory, Department of Computer Science, The University of Texas at Dallas, Richardson, TX, United States |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1431346/pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Camera-view supervision for bird's-eye-view semantic segmentation |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10531 |
| 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 Vision and Imaging |
| related_works | https://openalex.org/W2348909947, https://openalex.org/W4292672442, https://openalex.org/W2362101859, https://openalex.org/W2791431590, https://openalex.org/W2941610985, https://openalex.org/W4235810826, https://openalex.org/W3000105423, https://openalex.org/W2350688482, https://openalex.org/W2122135111, https://openalex.org/W2053575972 |
| cited_by_count | 0 |
| locations_count | 4 |
| best_oa_location.id | doi:10.3389/fdata.2024.1431346 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210201220 |
| best_oa_location.source.issn | 2624-909X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2624-909X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Frontiers in Big Data |
| best_oa_location.source.host_organization | https://openalex.org/P4310320527 |
| best_oa_location.source.host_organization_name | Frontiers Media |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320527 |
| best_oa_location.source.host_organization_lineage_names | Frontiers Media |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1431346/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 | Frontiers in Big Data |
| best_oa_location.landing_page_url | https://doi.org/10.3389/fdata.2024.1431346 |
| primary_location.id | doi:10.3389/fdata.2024.1431346 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210201220 |
| primary_location.source.issn | 2624-909X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2624-909X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Frontiers in Big Data |
| primary_location.source.host_organization | https://openalex.org/P4310320527 |
| primary_location.source.host_organization_name | Frontiers Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320527 |
| primary_location.source.host_organization_lineage_names | Frontiers Media |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2024.1431346/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 | Frontiers in Big Data |
| primary_location.landing_page_url | https://doi.org/10.3389/fdata.2024.1431346 |
| publication_date | 2024-11-15 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W3035574168, https://openalex.org/W2412782625, https://openalex.org/W6753801691, https://openalex.org/W6839067270, https://openalex.org/W6685261749, https://openalex.org/W2102605133, https://openalex.org/W2520707372, https://openalex.org/W2985775862, https://openalex.org/W3193266637, https://openalex.org/W6845097741, https://openalex.org/W2194775991, https://openalex.org/W4214530037, https://openalex.org/W6631190155, https://openalex.org/W4402716300, https://openalex.org/W2963591054, https://openalex.org/W4382464460, https://openalex.org/W2963351448, https://openalex.org/W1803059841, https://openalex.org/W4383066393, https://openalex.org/W2972211064, https://openalex.org/W2948515602, https://openalex.org/W3109395584, https://openalex.org/W6763422710, https://openalex.org/W6739778489, https://openalex.org/W4214520160, https://openalex.org/W3034669477, https://openalex.org/W6756392615, https://openalex.org/W6683067110, https://openalex.org/W2139905387, https://openalex.org/W2964054038, https://openalex.org/W4387415226, https://openalex.org/W6762718338, https://openalex.org/W6810240388, https://openalex.org/W6839180059, https://openalex.org/W4312641958, https://openalex.org/W2963727135, https://openalex.org/W4289283268, https://openalex.org/W3102343027, https://openalex.org/W2158211626, https://openalex.org/W4281848959, https://openalex.org/W2888531279, https://openalex.org/W2560609797, https://openalex.org/W1522301498, https://openalex.org/W4224259431, https://openalex.org/W2951234442, https://openalex.org/W4284701604, https://openalex.org/W2604213426, https://openalex.org/W2963121255 |
| referenced_works_count | 48 |
| abstract_inverted_index.. | 124 |
| abstract_inverted_index.a | 5, 38, 72, 82 |
| abstract_inverted_index.32 | 97 |
| abstract_inverted_index.We | 36 |
| abstract_inverted_index.at | 122 |
| abstract_inverted_index.be | 120 |
| abstract_inverted_index.in | 15, 60, 75, 85 |
| abstract_inverted_index.is | 4 |
| abstract_inverted_index.of | 10, 41, 55, 96 |
| abstract_inverted_index.on | 22, 67 |
| abstract_inverted_index.to | 25, 29, 89 |
| abstract_inverted_index.Our | 64 |
| abstract_inverted_index.The | 112 |
| abstract_inverted_index.and | 7, 12, 32, 48, 58, 81, 104, 114, 117 |
| abstract_inverted_index.can | 119 |
| abstract_inverted_index.for | 78, 107 |
| abstract_inverted_index.the | 53, 61, 68 |
| abstract_inverted_index.3.8% | 73 |
| abstract_inverted_index.FPS. | 98 |
| abstract_inverted_index.This | 99 |
| abstract_inverted_index.code | 118 |
| abstract_inverted_index.many | 16 |
| abstract_inverted_index.more | 102 |
| abstract_inverted_index.rely | 21 |
| abstract_inverted_index.with | 45 |
| abstract_inverted_index.(IoU) | 77 |
| abstract_inverted_index.BEVSS | 62, 106 |
| abstract_inverted_index.codes | 113 |
| abstract_inverted_index.depth | 47, 86 |
| abstract_inverted_index.error | 87 |
| abstract_inverted_index.found | 121 |
| abstract_inverted_index.novel | 39 |
| abstract_inverted_index.shows | 71 |
| abstract_inverted_index.times | 95 |
| abstract_inverted_index.train | 26 |
| abstract_inverted_index.which | 51 |
| abstract_inverted_index.while | 91 |
| abstract_inverted_index.method | 40, 100 |
| abstract_inverted_index.model, | 65 |
| abstract_inverted_index.offers | 101 |
| abstract_inverted_index.(BEVSS) | 3 |
| abstract_inverted_index.30-fold | 83 |
| abstract_inverted_index.Current | 19 |
| abstract_inverted_index.control | 13 |
| abstract_inverted_index.crucial | 8 |
| abstract_inverted_index.details | 116 |
| abstract_inverted_index.driving | 110 |
| abstract_inverted_index.feature | 43, 56 |
| abstract_inverted_index.leading | 28 |
| abstract_inverted_index.methods | 20 |
| abstract_inverted_index.models, | 27 |
| abstract_inverted_index.propose | 37 |
| abstract_inverted_index.quality | 54 |
| abstract_inverted_index.systems | 14 |
| abstract_inverted_index.vehicle | 79 |
| abstract_inverted_index.Semantic | 1 |
| abstract_inverted_index.accurate | 103 |
| abstract_inverted_index.compared | 88 |
| abstract_inverted_index.dataset, | 70 |
| abstract_inverted_index.improves | 52 |
| abstract_inverted_index.learning | 24 |
| abstract_inverted_index.nuScenes | 69 |
| abstract_inverted_index.planning | 11 |
| abstract_inverted_index.powerful | 6 |
| abstract_inverted_index.reliable | 105 |
| abstract_inverted_index.systems. | 111 |
| abstract_inverted_index.component | 9 |
| abstract_inverted_index.evaluated | 66 |
| abstract_inverted_index.inference | 94 |
| abstract_inverted_index.pipeline. | 63 |
| abstract_inverted_index.real-time | 108 |
| abstract_inverted_index.reduction | 84 |
| abstract_inverted_index.vehicles. | 18 |
| abstract_inverted_index.autonomous | 17, 109 |
| abstract_inverted_index.baselines, | 90 |
| abstract_inverted_index.end-to-end | 23 |
| abstract_inverted_index.extraction | 44, 57 |
| abstract_inverted_index.inaccurate | 33 |
| abstract_inverted_index.indirectly | 30 |
| abstract_inverted_index.projection | 59 |
| abstract_inverted_index.supervised | 31 |
| abstract_inverted_index.camera-view | 46 |
| abstract_inverted_index.competitive | 93 |
| abstract_inverted_index.improvement | 74 |
| abstract_inverted_index.maintaining | 92 |
| abstract_inverted_index.supervising | 42 |
| abstract_inverted_index.Segmentation | 2 |
| abstract_inverted_index.information, | 50 |
| abstract_inverted_index.projections. | 35 |
| abstract_inverted_index.segmentation | 49, 80 |
| abstract_inverted_index.camera-to-BEV | 34 |
| abstract_inverted_index.implementation | 115 |
| abstract_inverted_index.Bird's-eye-view | 0 |
| abstract_inverted_index.Intersection-over-Union | 76 |
| abstract_inverted_index.https://github.com/bluffish/sucam | 123 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5028766722 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I162577319 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.25885629 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |