KRONC: Keypoint-based Robust Camera Optimization for 3D Car Reconstruction Article Swipe
Davide Di Nucci
,
Alessandro Simoni
,
Matteo Tomei
,
Luca Ciuffreda
,
Roberto Vezzani
,
Rita Cucchiara
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.05407
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.05407
The three-dimensional representation of objects or scenes starting from a set of images has been a widely discussed topic for years and has gained additional attention after the diffusion of NeRF-based approaches. However, an underestimated prerequisite is the knowledge of camera poses or, more specifically, the estimation of the extrinsic calibration parameters. Although excellent general-purpose Structure-from-Motion methods are available as a pre-processing step, their computational load is high and they require a lot of frames to guarantee sufficient overlapping among the views. This paper introduces KRONC, a novel approach aimed at inferring view poses by leveraging prior knowledge about the object to reconstruct and its representation through semantic keypoints. With a focus on vehicle scenes, KRONC is able to estimate the position of the views as a solution to a light optimization problem targeting the convergence of keypoints' back-projections to a singular point. To validate the method, a specific dataset of real-world car scenes has been collected. Experiments confirm KRONC's ability to generate excellent estimates of camera poses starting from very coarse initialization. Results are comparable with Structure-from-Motion methods with huge savings in computation. Code and data will be made publicly available.
Related Topics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.05407
- https://arxiv.org/pdf/2409.05407
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403618126
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4403618126Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2409.05407Digital Object Identifier
- Title
-
KRONC: Keypoint-based Robust Camera Optimization for 3D Car ReconstructionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-09Full publication date if available
- Authors
-
Davide Di Nucci, Alessandro Simoni, Matteo Tomei, Luca Ciuffreda, Roberto Vezzani, Rita CucchiaraList of authors in order
- Landing page
-
https://arxiv.org/abs/2409.05407Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2409.05407Direct 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/2409.05407Direct OA link when available
- Concepts
-
Computer vision, Artificial intelligence, Computer scienceTop 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/W4403618126 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2409.05407 |
| ids.doi | https://doi.org/10.48550/arxiv.2409.05407 |
| ids.openalex | https://openalex.org/W4403618126 |
| fwci | |
| type | preprint |
| title | KRONC: Keypoint-based Robust Camera Optimization for 3D Car Reconstruction |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| 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 | 0.9957000017166138 |
| 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/T10191 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9868999719619751 |
| 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/T10481 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9778000116348267 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1704 |
| topics[2].subfield.display_name | Computer Graphics and Computer-Aided Design |
| topics[2].display_name | Computer Graphics and Visualization Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C31972630 |
| concepts[0].level | 1 |
| concepts[0].score | 0.6186299324035645 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[0].display_name | Computer vision |
| concepts[1].id | https://openalex.org/C154945302 |
| concepts[1].level | 1 |
| concepts[1].score | 0.5821345448493958 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[1].display_name | Artificial intelligence |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.5572388172149658 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| keywords[0].id | https://openalex.org/keywords/computer-vision |
| keywords[0].score | 0.6186299324035645 |
| keywords[0].display_name | Computer vision |
| keywords[1].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[1].score | 0.5821345448493958 |
| keywords[1].display_name | Artificial intelligence |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.5572388172149658 |
| keywords[2].display_name | Computer science |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2409.05407 |
| 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/2409.05407 |
| 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/2409.05407 |
| locations[1].id | doi:10.48550/arxiv.2409.05407 |
| 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 | cc-by |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| 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.2409.05407 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5092543610 |
| authorships[0].author.orcid | https://orcid.org/0009-0000-7450-8796 |
| authorships[0].author.display_name | Davide Di Nucci |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Di Nucci, Davide |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5019304399 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3051-5083 |
| authorships[1].author.display_name | Alessandro Simoni |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Simoni, Alessandro |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5018404181 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-1385-924X |
| authorships[2].author.display_name | Matteo Tomei |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Tomei, Matteo |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5081089602 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Luca Ciuffreda |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Ciuffreda, Luca |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5081341599 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-1046-6870 |
| authorships[4].author.display_name | Roberto Vezzani |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Vezzani, Roberto |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5030948871 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-2239-283X |
| authorships[5].author.display_name | Rita Cucchiara |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Cucchiara, Rita |
| authorships[5].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/2409.05407 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | KRONC: Keypoint-based Robust Camera Optimization for 3D Car Reconstruction |
| 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 | 0.9957000017166138 |
| 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/W2772917594, https://openalex.org/W2036807459, https://openalex.org/W2058170566, https://openalex.org/W2755342338, https://openalex.org/W2166024367, https://openalex.org/W3116076068, https://openalex.org/W2229312674, https://openalex.org/W2951359407, https://openalex.org/W2079911747, https://openalex.org/W1969923398 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2409.05407 |
| 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/2409.05407 |
| 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/2409.05407 |
| primary_location.id | pmh:oai:arXiv.org:2409.05407 |
| 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/2409.05407 |
| 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/2409.05407 |
| publication_date | 2024-09-09 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 9, 15, 60, 71, 86, 110, 126, 129, 140, 147 |
| abstract_inverted_index.To | 143 |
| abstract_inverted_index.an | 33 |
| abstract_inverted_index.as | 59, 125 |
| abstract_inverted_index.at | 90 |
| abstract_inverted_index.be | 188 |
| abstract_inverted_index.by | 94 |
| abstract_inverted_index.in | 182 |
| abstract_inverted_index.is | 36, 66, 116 |
| abstract_inverted_index.of | 3, 11, 29, 39, 47, 73, 122, 136, 150, 165 |
| abstract_inverted_index.on | 112 |
| abstract_inverted_index.or | 5 |
| abstract_inverted_index.to | 75, 101, 118, 128, 139, 161 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.and | 21, 68, 103, 185 |
| abstract_inverted_index.are | 57, 174 |
| abstract_inverted_index.car | 152 |
| abstract_inverted_index.for | 19 |
| abstract_inverted_index.has | 13, 22, 154 |
| abstract_inverted_index.its | 104 |
| abstract_inverted_index.lot | 72 |
| abstract_inverted_index.or, | 42 |
| abstract_inverted_index.set | 10 |
| abstract_inverted_index.the | 27, 37, 45, 48, 80, 99, 120, 123, 134, 145 |
| abstract_inverted_index.Code | 184 |
| abstract_inverted_index.This | 82 |
| abstract_inverted_index.With | 109 |
| abstract_inverted_index.able | 117 |
| abstract_inverted_index.been | 14, 155 |
| abstract_inverted_index.data | 186 |
| abstract_inverted_index.from | 8, 169 |
| abstract_inverted_index.high | 67 |
| abstract_inverted_index.huge | 180 |
| abstract_inverted_index.load | 65 |
| abstract_inverted_index.made | 189 |
| abstract_inverted_index.more | 43 |
| abstract_inverted_index.they | 69 |
| abstract_inverted_index.very | 170 |
| abstract_inverted_index.view | 92 |
| abstract_inverted_index.will | 187 |
| abstract_inverted_index.with | 176, 179 |
| abstract_inverted_index.KRONC | 115 |
| abstract_inverted_index.about | 98 |
| abstract_inverted_index.after | 26 |
| abstract_inverted_index.aimed | 89 |
| abstract_inverted_index.among | 79 |
| abstract_inverted_index.focus | 111 |
| abstract_inverted_index.light | 130 |
| abstract_inverted_index.novel | 87 |
| abstract_inverted_index.paper | 83 |
| abstract_inverted_index.poses | 41, 93, 167 |
| abstract_inverted_index.prior | 96 |
| abstract_inverted_index.step, | 62 |
| abstract_inverted_index.their | 63 |
| abstract_inverted_index.topic | 18 |
| abstract_inverted_index.views | 124 |
| abstract_inverted_index.years | 20 |
| abstract_inverted_index.KRONC, | 85 |
| abstract_inverted_index.camera | 40, 166 |
| abstract_inverted_index.coarse | 171 |
| abstract_inverted_index.frames | 74 |
| abstract_inverted_index.gained | 23 |
| abstract_inverted_index.images | 12 |
| abstract_inverted_index.object | 100 |
| abstract_inverted_index.point. | 142 |
| abstract_inverted_index.scenes | 6, 153 |
| abstract_inverted_index.views. | 81 |
| abstract_inverted_index.widely | 16 |
| abstract_inverted_index.KRONC's | 159 |
| abstract_inverted_index.Results | 173 |
| abstract_inverted_index.ability | 160 |
| abstract_inverted_index.confirm | 158 |
| abstract_inverted_index.dataset | 149 |
| abstract_inverted_index.method, | 146 |
| abstract_inverted_index.methods | 56, 178 |
| abstract_inverted_index.objects | 4 |
| abstract_inverted_index.problem | 132 |
| abstract_inverted_index.require | 70 |
| abstract_inverted_index.savings | 181 |
| abstract_inverted_index.scenes, | 114 |
| abstract_inverted_index.through | 106 |
| abstract_inverted_index.vehicle | 113 |
| abstract_inverted_index.Although | 52 |
| abstract_inverted_index.However, | 32 |
| abstract_inverted_index.approach | 88 |
| abstract_inverted_index.estimate | 119 |
| abstract_inverted_index.generate | 162 |
| abstract_inverted_index.position | 121 |
| abstract_inverted_index.publicly | 190 |
| abstract_inverted_index.semantic | 107 |
| abstract_inverted_index.singular | 141 |
| abstract_inverted_index.solution | 127 |
| abstract_inverted_index.specific | 148 |
| abstract_inverted_index.starting | 7, 168 |
| abstract_inverted_index.validate | 144 |
| abstract_inverted_index.attention | 25 |
| abstract_inverted_index.available | 58 |
| abstract_inverted_index.diffusion | 28 |
| abstract_inverted_index.discussed | 17 |
| abstract_inverted_index.estimates | 164 |
| abstract_inverted_index.excellent | 53, 163 |
| abstract_inverted_index.extrinsic | 49 |
| abstract_inverted_index.guarantee | 76 |
| abstract_inverted_index.inferring | 91 |
| abstract_inverted_index.knowledge | 38, 97 |
| abstract_inverted_index.targeting | 133 |
| abstract_inverted_index.NeRF-based | 30 |
| abstract_inverted_index.additional | 24 |
| abstract_inverted_index.available. | 191 |
| abstract_inverted_index.collected. | 156 |
| abstract_inverted_index.comparable | 175 |
| abstract_inverted_index.estimation | 46 |
| abstract_inverted_index.introduces | 84 |
| abstract_inverted_index.keypoints' | 137 |
| abstract_inverted_index.keypoints. | 108 |
| abstract_inverted_index.leveraging | 95 |
| abstract_inverted_index.real-world | 151 |
| abstract_inverted_index.sufficient | 77 |
| abstract_inverted_index.Experiments | 157 |
| abstract_inverted_index.approaches. | 31 |
| abstract_inverted_index.calibration | 50 |
| abstract_inverted_index.convergence | 135 |
| abstract_inverted_index.overlapping | 78 |
| abstract_inverted_index.parameters. | 51 |
| abstract_inverted_index.reconstruct | 102 |
| abstract_inverted_index.computation. | 183 |
| abstract_inverted_index.optimization | 131 |
| abstract_inverted_index.prerequisite | 35 |
| abstract_inverted_index.computational | 64 |
| abstract_inverted_index.specifically, | 44 |
| abstract_inverted_index.pre-processing | 61 |
| abstract_inverted_index.representation | 2, 105 |
| abstract_inverted_index.underestimated | 34 |
| abstract_inverted_index.general-purpose | 54 |
| abstract_inverted_index.initialization. | 172 |
| abstract_inverted_index.back-projections | 138 |
| abstract_inverted_index.three-dimensional | 1 |
| abstract_inverted_index.Structure-from-Motion | 55, 177 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |