3D recognition based on ordered images reconstruction Article Swipe
Ning Zhang
,
Yongjia Zhao
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1051/matecconf/201823202045
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1051/matecconf/201823202045
Nowadays, more and more applications require precise and quickly 3D recognition, such as augmented reality and robot navigation. In recent years, model-based methods can get accurate object or scene recognition, but it takes a lot of time to reconstruct the model. Therefore, we propose a fast 3D reconstruction method based on ordered images for robust and accurate 3D recognition. The proposed algorithm consists of two parts, the offline processing stage, and the online processing stage. First, in the offline processing stage, the sparse point cloud model of the scene or object is reconstructed based on the sequential images, optimized using the BA algorithm based on the local correlation frame, and then the local descriptor of the resulting model points is stored. Secondly, in the online processing stage, for each image frame of the camera video, a matching relationship between the stored point cloud and the 2D feature points on the image frame is established, based on which the pose of the camera can be solved accurately.
Related Topics
Concepts
Artificial intelligence
Computer vision
Computer science
Point cloud
Frame (networking)
Feature (linguistics)
Matching (statistics)
Object (grammar)
Cognitive neuroscience of visual object recognition
Image processing
Point (geometry)
Image (mathematics)
Mathematics
Philosophy
Geometry
Linguistics
Telecommunications
Statistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1051/matecconf/201823202045
- https://www.matec-conferences.org/articles/matecconf/pdf/2018/91/matecconf_eitce2018_02045.pdf
- OA Status
- diamond
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2900636896
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2900636896Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1051/matecconf/201823202045Digital Object Identifier
- Title
-
3D recognition based on ordered images reconstructionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-01-01Full publication date if available
- Authors
-
Ning Zhang, Yongjia ZhaoList of authors in order
- Landing page
-
https://doi.org/10.1051/matecconf/201823202045Publisher landing page
- PDF URL
-
https://www.matec-conferences.org/articles/matecconf/pdf/2018/91/matecconf_eitce2018_02045.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.matec-conferences.org/articles/matecconf/pdf/2018/91/matecconf_eitce2018_02045.pdfDirect OA link when available
- Concepts
-
Artificial intelligence, Computer vision, Computer science, Point cloud, Frame (networking), Feature (linguistics), Matching (statistics), Object (grammar), Cognitive neuroscience of visual object recognition, Image processing, Point (geometry), Image (mathematics), Mathematics, Philosophy, Geometry, Linguistics, Telecommunications, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
12Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2900636896 |
|---|---|
| doi | https://doi.org/10.1051/matecconf/201823202045 |
| ids.doi | https://doi.org/10.1051/matecconf/201823202045 |
| ids.mag | 2900636896 |
| ids.openalex | https://openalex.org/W2900636896 |
| fwci | 0.0 |
| type | article |
| title | 3D recognition based on ordered images reconstruction |
| biblio.issue | |
| biblio.volume | 232 |
| biblio.last_page | 02045 |
| biblio.first_page | 02045 |
| topics[0].id | https://openalex.org/T10191 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2202 |
| topics[0].subfield.display_name | Aerospace Engineering |
| topics[0].display_name | Robotics and Sensor-Based Localization |
| topics[1].id | https://openalex.org/T10627 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9993000030517578 |
| 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 Image and Video Retrieval Techniques |
| topics[2].id | https://openalex.org/T12549 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9965000152587891 |
| 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 | Image and Object Detection Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C154945302 |
| concepts[0].level | 1 |
| concepts[0].score | 0.8238900899887085 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[0].display_name | Artificial intelligence |
| concepts[1].id | https://openalex.org/C31972630 |
| concepts[1].level | 1 |
| concepts[1].score | 0.7873959541320801 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[1].display_name | Computer vision |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.7808293104171753 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C131979681 |
| concepts[3].level | 2 |
| concepts[3].score | 0.704024612903595 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1899648 |
| concepts[3].display_name | Point cloud |
| concepts[4].id | https://openalex.org/C126042441 |
| concepts[4].level | 2 |
| concepts[4].score | 0.594545304775238 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1324888 |
| concepts[4].display_name | Frame (networking) |
| concepts[5].id | https://openalex.org/C2776401178 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5480003356933594 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[5].display_name | Feature (linguistics) |
| concepts[6].id | https://openalex.org/C165064840 |
| concepts[6].level | 2 |
| concepts[6].score | 0.47855573892593384 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1321061 |
| concepts[6].display_name | Matching (statistics) |
| concepts[7].id | https://openalex.org/C2781238097 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4496142268180847 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q175026 |
| concepts[7].display_name | Object (grammar) |
| concepts[8].id | https://openalex.org/C64876066 |
| concepts[8].level | 3 |
| concepts[8].score | 0.43333736062049866 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q5141226 |
| concepts[8].display_name | Cognitive neuroscience of visual object recognition |
| concepts[9].id | https://openalex.org/C9417928 |
| concepts[9].level | 3 |
| concepts[9].score | 0.426209419965744 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1070689 |
| concepts[9].display_name | Image processing |
| concepts[10].id | https://openalex.org/C28719098 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4226640462875366 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q44946 |
| concepts[10].display_name | Point (geometry) |
| concepts[11].id | https://openalex.org/C115961682 |
| concepts[11].level | 2 |
| concepts[11].score | 0.3062070608139038 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[11].display_name | Image (mathematics) |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.09560912847518921 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C138885662 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[13].display_name | Philosophy |
| concepts[14].id | https://openalex.org/C2524010 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[14].display_name | Geometry |
| concepts[15].id | https://openalex.org/C41895202 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[15].display_name | Linguistics |
| concepts[16].id | https://openalex.org/C76155785 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[16].display_name | Telecommunications |
| concepts[17].id | https://openalex.org/C105795698 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[17].display_name | Statistics |
| keywords[0].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[0].score | 0.8238900899887085 |
| keywords[0].display_name | Artificial intelligence |
| keywords[1].id | https://openalex.org/keywords/computer-vision |
| keywords[1].score | 0.7873959541320801 |
| keywords[1].display_name | Computer vision |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.7808293104171753 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/point-cloud |
| keywords[3].score | 0.704024612903595 |
| keywords[3].display_name | Point cloud |
| keywords[4].id | https://openalex.org/keywords/frame |
| keywords[4].score | 0.594545304775238 |
| keywords[4].display_name | Frame (networking) |
| keywords[5].id | https://openalex.org/keywords/feature |
| keywords[5].score | 0.5480003356933594 |
| keywords[5].display_name | Feature (linguistics) |
| keywords[6].id | https://openalex.org/keywords/matching |
| keywords[6].score | 0.47855573892593384 |
| keywords[6].display_name | Matching (statistics) |
| keywords[7].id | https://openalex.org/keywords/object |
| keywords[7].score | 0.4496142268180847 |
| keywords[7].display_name | Object (grammar) |
| keywords[8].id | https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition |
| keywords[8].score | 0.43333736062049866 |
| keywords[8].display_name | Cognitive neuroscience of visual object recognition |
| keywords[9].id | https://openalex.org/keywords/image-processing |
| keywords[9].score | 0.426209419965744 |
| keywords[9].display_name | Image processing |
| keywords[10].id | https://openalex.org/keywords/point |
| keywords[10].score | 0.4226640462875366 |
| keywords[10].display_name | Point (geometry) |
| keywords[11].id | https://openalex.org/keywords/image |
| keywords[11].score | 0.3062070608139038 |
| keywords[11].display_name | Image (mathematics) |
| keywords[12].id | https://openalex.org/keywords/mathematics |
| keywords[12].score | 0.09560912847518921 |
| keywords[12].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1051/matecconf/201823202045 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210175061 |
| locations[0].source.issn | 2261-236X, 2274-7214 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2261-236X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | MATEC Web of Conferences |
| locations[0].source.host_organization | https://openalex.org/P4310319748 |
| locations[0].source.host_organization_name | EDP Sciences |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319748 |
| locations[0].source.host_organization_lineage_names | EDP Sciences |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.matec-conferences.org/articles/matecconf/pdf/2018/91/matecconf_eitce2018_02045.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 | MATEC Web of Conferences |
| locations[0].landing_page_url | https://doi.org/10.1051/matecconf/201823202045 |
| locations[1].id | pmh:oai:doaj.org/article:5b9b97b71c7d4fa2814217f0c08382f0 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401280 |
| 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 | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | MATEC Web of Conferences, Vol 232, p 02045 (2018) |
| locations[1].landing_page_url | https://doaj.org/article/5b9b97b71c7d4fa2814217f0c08382f0 |
| locations[2].id | pmh:oai:edpsciences.org:dkey/10.1051/matecconf/201823202045 |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306400744 |
| 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 | Springer Link (Chiba Institute of Technology) |
| locations[2].source.host_organization | https://openalex.org/I8488066 |
| locations[2].source.host_organization_name | Chiba Institute of Technology |
| locations[2].source.host_organization_lineage | https://openalex.org/I8488066 |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | https://doi.org/10.1051/matecconf/201823202045 |
| locations[2].landing_page_url | |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5068059545 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1659-2907 |
| authorships[0].author.display_name | Ning Zhang |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I82880672 |
| authorships[0].affiliations[0].raw_affiliation_string | State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China |
| authorships[0].institutions[0].id | https://openalex.org/I82880672 |
| authorships[0].institutions[0].ror | https://ror.org/00wk2mp56 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I82880672 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Beihang University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ning Zhang |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China |
| authorships[1].author.id | https://openalex.org/A5082426267 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4557-9066 |
| authorships[1].author.display_name | Yongjia Zhao |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I82880672 |
| authorships[1].affiliations[0].raw_affiliation_string | State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China |
| authorships[1].institutions[0].id | https://openalex.org/I82880672 |
| authorships[1].institutions[0].ror | https://ror.org/00wk2mp56 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I82880672 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Beihang University |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | YongJia Zhao |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.matec-conferences.org/articles/matecconf/pdf/2018/91/matecconf_eitce2018_02045.pdf |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2018-11-29T00:00:00 |
| display_name | 3D recognition based on ordered images reconstruction |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10191 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2202 |
| primary_topic.subfield.display_name | Aerospace Engineering |
| primary_topic.display_name | Robotics and Sensor-Based Localization |
| related_works | https://openalex.org/W4389574804, https://openalex.org/W3016928466, https://openalex.org/W2936725271, https://openalex.org/W3150655618, https://openalex.org/W3108295644, https://openalex.org/W1578717197, https://openalex.org/W2626737336, https://openalex.org/W2005998065, https://openalex.org/W2980582925, https://openalex.org/W3126423817 |
| cited_by_count | 0 |
| locations_count | 3 |
| best_oa_location.id | doi:10.1051/matecconf/201823202045 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210175061 |
| best_oa_location.source.issn | 2261-236X, 2274-7214 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2261-236X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | MATEC Web of Conferences |
| best_oa_location.source.host_organization | https://openalex.org/P4310319748 |
| best_oa_location.source.host_organization_name | EDP Sciences |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319748 |
| best_oa_location.source.host_organization_lineage_names | EDP Sciences |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.matec-conferences.org/articles/matecconf/pdf/2018/91/matecconf_eitce2018_02045.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 | MATEC Web of Conferences |
| best_oa_location.landing_page_url | https://doi.org/10.1051/matecconf/201823202045 |
| primary_location.id | doi:10.1051/matecconf/201823202045 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210175061 |
| primary_location.source.issn | 2261-236X, 2274-7214 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2261-236X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | MATEC Web of Conferences |
| primary_location.source.host_organization | https://openalex.org/P4310319748 |
| primary_location.source.host_organization_name | EDP Sciences |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319748 |
| primary_location.source.host_organization_lineage_names | EDP Sciences |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.matec-conferences.org/articles/matecconf/pdf/2018/91/matecconf_eitce2018_02045.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 | MATEC Web of Conferences |
| primary_location.landing_page_url | https://doi.org/10.1051/matecconf/201823202045 |
| publication_date | 2018-01-01 |
| publication_year | 2018 |
| referenced_works | https://openalex.org/W2766875746, https://openalex.org/W2123373885, https://openalex.org/W2123537152, https://openalex.org/W1627400044, https://openalex.org/W2151103935, https://openalex.org/W2096077837, https://openalex.org/W2036273537, https://openalex.org/W2085261163, https://openalex.org/W2127972053, https://openalex.org/W2033819227, https://openalex.org/W2471962767, https://openalex.org/W3157598734 |
| referenced_works_count | 12 |
| abstract_inverted_index.a | 33, 44, 135 |
| abstract_inverted_index.2D | 145 |
| abstract_inverted_index.3D | 9, 46, 57 |
| abstract_inverted_index.BA | 101 |
| abstract_inverted_index.In | 18 |
| abstract_inverted_index.as | 12 |
| abstract_inverted_index.be | 163 |
| abstract_inverted_index.in | 76, 122 |
| abstract_inverted_index.is | 91, 119, 152 |
| abstract_inverted_index.it | 31 |
| abstract_inverted_index.of | 35, 63, 86, 114, 131, 159 |
| abstract_inverted_index.on | 50, 94, 104, 148, 155 |
| abstract_inverted_index.or | 27, 89 |
| abstract_inverted_index.to | 37 |
| abstract_inverted_index.we | 42 |
| abstract_inverted_index.The | 59 |
| abstract_inverted_index.and | 2, 7, 15, 55, 70, 109, 143 |
| abstract_inverted_index.but | 30 |
| abstract_inverted_index.can | 23, 162 |
| abstract_inverted_index.for | 53, 127 |
| abstract_inverted_index.get | 24 |
| abstract_inverted_index.lot | 34 |
| abstract_inverted_index.the | 39, 66, 71, 77, 81, 87, 95, 100, 105, 111, 115, 123, 132, 139, 144, 149, 157, 160 |
| abstract_inverted_index.two | 64 |
| abstract_inverted_index.each | 128 |
| abstract_inverted_index.fast | 45 |
| abstract_inverted_index.more | 1, 3 |
| abstract_inverted_index.pose | 158 |
| abstract_inverted_index.such | 11 |
| abstract_inverted_index.then | 110 |
| abstract_inverted_index.time | 36 |
| abstract_inverted_index.based | 49, 93, 103, 154 |
| abstract_inverted_index.cloud | 84, 142 |
| abstract_inverted_index.frame | 130, 151 |
| abstract_inverted_index.image | 129, 150 |
| abstract_inverted_index.local | 106, 112 |
| abstract_inverted_index.model | 85, 117 |
| abstract_inverted_index.point | 83, 141 |
| abstract_inverted_index.robot | 16 |
| abstract_inverted_index.scene | 28, 88 |
| abstract_inverted_index.takes | 32 |
| abstract_inverted_index.using | 99 |
| abstract_inverted_index.which | 156 |
| abstract_inverted_index.First, | 75 |
| abstract_inverted_index.camera | 133, 161 |
| abstract_inverted_index.frame, | 108 |
| abstract_inverted_index.images | 52 |
| abstract_inverted_index.method | 48 |
| abstract_inverted_index.model. | 40 |
| abstract_inverted_index.object | 26, 90 |
| abstract_inverted_index.online | 72, 124 |
| abstract_inverted_index.parts, | 65 |
| abstract_inverted_index.points | 118, 147 |
| abstract_inverted_index.recent | 19 |
| abstract_inverted_index.robust | 54 |
| abstract_inverted_index.solved | 164 |
| abstract_inverted_index.sparse | 82 |
| abstract_inverted_index.stage, | 69, 80, 126 |
| abstract_inverted_index.stage. | 74 |
| abstract_inverted_index.stored | 140 |
| abstract_inverted_index.video, | 134 |
| abstract_inverted_index.years, | 20 |
| abstract_inverted_index.between | 138 |
| abstract_inverted_index.feature | 146 |
| abstract_inverted_index.images, | 97 |
| abstract_inverted_index.methods | 22 |
| abstract_inverted_index.offline | 67, 78 |
| abstract_inverted_index.ordered | 51 |
| abstract_inverted_index.precise | 6 |
| abstract_inverted_index.propose | 43 |
| abstract_inverted_index.quickly | 8 |
| abstract_inverted_index.reality | 14 |
| abstract_inverted_index.require | 5 |
| abstract_inverted_index.stored. | 120 |
| abstract_inverted_index.accurate | 25, 56 |
| abstract_inverted_index.consists | 62 |
| abstract_inverted_index.matching | 136 |
| abstract_inverted_index.proposed | 60 |
| abstract_inverted_index.Nowadays, | 0 |
| abstract_inverted_index.Secondly, | 121 |
| abstract_inverted_index.algorithm | 61, 102 |
| abstract_inverted_index.augmented | 13 |
| abstract_inverted_index.optimized | 98 |
| abstract_inverted_index.resulting | 116 |
| abstract_inverted_index.Therefore, | 41 |
| abstract_inverted_index.descriptor | 113 |
| abstract_inverted_index.processing | 68, 73, 79, 125 |
| abstract_inverted_index.sequential | 96 |
| abstract_inverted_index.accurately. | 165 |
| abstract_inverted_index.correlation | 107 |
| abstract_inverted_index.model-based | 21 |
| abstract_inverted_index.navigation. | 17 |
| abstract_inverted_index.reconstruct | 38 |
| abstract_inverted_index.applications | 4 |
| abstract_inverted_index.established, | 153 |
| abstract_inverted_index.recognition, | 10, 29 |
| abstract_inverted_index.recognition. | 58 |
| abstract_inverted_index.relationship | 137 |
| abstract_inverted_index.reconstructed | 92 |
| abstract_inverted_index.reconstruction | 47 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.07801431 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |