Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving Article Swipe
Yichen Xie
,
Hongge Chen
,
Gregory P. Meyer
,
Yong Jae Lee
,
Eric M. Wolff
,
Masayoshi Tomizuka
,
Wei Zhan
,
Yuning Chai
,
Xin Huang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.15583
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.15583
Due to the lack of depth cues in images, multi-frame inputs are important for the success of vision-based perception, prediction, and planning in autonomous driving. Observations from different angles enable the recovery of 3D object states from 2D image inputs if we can identify the same instance in different input frames. However, the dynamic nature of autonomous driving scenes leads to significant changes in the appearance and shape of each instance captured by the camera at different time steps. To this end, we propose a novel contrastive learning algorithm, Cohere3D, to learn coherent instance representations in a long-term input sequence robust to the change in distance and perspective. The learned representation aids in instance-level correspondence across multiple input frames in downstream tasks. In the pretraining stage, the raw point clouds from LiDAR sensors are utilized to construct the long-term temporal correspondence for each instance, which serves as guidance for the extraction of instance-level representation from the vision-based bird's eye-view (BEV) feature map. Cohere3D encourages a consistent representation for the same instance at different frames but distinguishes between representations of different instances. We evaluate our algorithm by finetuning the pretrained model on various downstream perception, prediction, and planning tasks. Results show a notable improvement in both data efficiency and task performance.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2402.15583
- https://arxiv.org/pdf/2402.15583
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392223437
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4392223437Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2402.15583Digital Object Identifier
- Title
-
Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous DrivingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-23Full publication date if available
- Authors
-
Yichen Xie, Hongge Chen, Gregory P. Meyer, Yong Jae Lee, Eric M. Wolff, Masayoshi Tomizuka, Wei Zhan, Yuning Chai, Xin HuangList of authors in order
- Landing page
-
https://arxiv.org/abs/2402.15583Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2402.15583Direct 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/2402.15583Direct OA link when available
- Concepts
-
Coherence (philosophical gambling strategy), Computer science, Artificial intelligence, Representation (politics), Unsupervised learning, Feature learning, Computer vision, Pattern recognition (psychology), Mathematics, Statistics, Politics, Law, Political 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/W4392223437 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2402.15583 |
| ids.doi | https://doi.org/10.48550/arxiv.2402.15583 |
| ids.openalex | https://openalex.org/W4392223437 |
| fwci | |
| type | preprint |
| title | Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10036 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9775000214576721 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Advanced Neural Network Applications |
| topics[1].id | https://openalex.org/T11307 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9742000102996826 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Domain Adaptation and Few-Shot Learning |
| topics[2].id | https://openalex.org/T10775 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9726999998092651 |
| 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 | Generative Adversarial Networks and Image Synthesis |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2781181686 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7048871517181396 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q4226068 |
| concepts[0].display_name | Coherence (philosophical gambling strategy) |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6498212814331055 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6176491379737854 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C2776359362 |
| concepts[3].level | 3 |
| concepts[3].score | 0.555388331413269 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2145286 |
| concepts[3].display_name | Representation (politics) |
| concepts[4].id | https://openalex.org/C8038995 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5127728581428528 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q1152135 |
| concepts[4].display_name | Unsupervised learning |
| concepts[5].id | https://openalex.org/C59404180 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4945323169231415 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q17013334 |
| concepts[5].display_name | Feature learning |
| concepts[6].id | https://openalex.org/C31972630 |
| concepts[6].level | 1 |
| concepts[6].score | 0.40961164236068726 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[6].display_name | Computer vision |
| concepts[7].id | https://openalex.org/C153180895 |
| concepts[7].level | 2 |
| concepts[7].score | 0.33926475048065186 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[7].display_name | Pattern recognition (psychology) |
| concepts[8].id | https://openalex.org/C33923547 |
| concepts[8].level | 0 |
| concepts[8].score | 0.11316418647766113 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[8].display_name | Mathematics |
| concepts[9].id | https://openalex.org/C105795698 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0469323992729187 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[9].display_name | Statistics |
| concepts[10].id | https://openalex.org/C94625758 |
| concepts[10].level | 2 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7163 |
| concepts[10].display_name | Politics |
| concepts[11].id | https://openalex.org/C199539241 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7748 |
| concepts[11].display_name | Law |
| concepts[12].id | https://openalex.org/C17744445 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q36442 |
| concepts[12].display_name | Political science |
| keywords[0].id | https://openalex.org/keywords/coherence |
| keywords[0].score | 0.7048871517181396 |
| keywords[0].display_name | Coherence (philosophical gambling strategy) |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6498212814331055 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.6176491379737854 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/representation |
| keywords[3].score | 0.555388331413269 |
| keywords[3].display_name | Representation (politics) |
| keywords[4].id | https://openalex.org/keywords/unsupervised-learning |
| keywords[4].score | 0.5127728581428528 |
| keywords[4].display_name | Unsupervised learning |
| keywords[5].id | https://openalex.org/keywords/feature-learning |
| keywords[5].score | 0.4945323169231415 |
| keywords[5].display_name | Feature learning |
| keywords[6].id | https://openalex.org/keywords/computer-vision |
| keywords[6].score | 0.40961164236068726 |
| keywords[6].display_name | Computer vision |
| keywords[7].id | https://openalex.org/keywords/pattern-recognition |
| keywords[7].score | 0.33926475048065186 |
| keywords[7].display_name | Pattern recognition (psychology) |
| keywords[8].id | https://openalex.org/keywords/mathematics |
| keywords[8].score | 0.11316418647766113 |
| keywords[8].display_name | Mathematics |
| keywords[9].id | https://openalex.org/keywords/statistics |
| keywords[9].score | 0.0469323992729187 |
| keywords[9].display_name | Statistics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2402.15583 |
| 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/2402.15583 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| 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/2402.15583 |
| locations[1].id | doi:10.48550/arxiv.2402.15583 |
| 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.2402.15583 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5046598816 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0974-5979 |
| authorships[0].author.display_name | Yichen Xie |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Xie, Yichen |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5062177930 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-7519-7341 |
| authorships[1].author.display_name | Hongge Chen |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Chen, Hongge |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5067267836 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-9444-5577 |
| authorships[2].author.display_name | Gregory P. Meyer |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Meyer, Gregory P. |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5112702739 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Yong Jae Lee |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Lee, Yong Jae |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5065869782 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Eric M. Wolff |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Wolff, Eric M. |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5064077634 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-0206-6639 |
| authorships[5].author.display_name | Masayoshi Tomizuka |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Tomizuka, Masayoshi |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5021239979 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-7167-6353 |
| authorships[6].author.display_name | Wei Zhan |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Zhan, Wei |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5079961895 |
| authorships[7].author.orcid | |
| authorships[7].author.display_name | Yuning Chai |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Chai, Yuning |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5101892149 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-5909-5935 |
| authorships[8].author.display_name | Xin Huang |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Huang, Xin |
| authorships[8].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/2402.15583 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2024-02-28T00:00:00 |
| display_name | Cohere3D: Exploiting Temporal Coherence for Unsupervised Representation Learning of Vision-based Autonomous Driving |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10036 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9775000214576721 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Advanced Neural Network Applications |
| related_works | https://openalex.org/W3174759195, https://openalex.org/W3167013339, https://openalex.org/W4287121366, https://openalex.org/W60493759, https://openalex.org/W3213069564, https://openalex.org/W4308619659, https://openalex.org/W2997229301, https://openalex.org/W2794802664, https://openalex.org/W4389072551, https://openalex.org/W4378421684 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2402.15583 |
| 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/2402.15583 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| 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/2402.15583 |
| primary_location.id | pmh:oai:arXiv.org:2402.15583 |
| 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/2402.15583 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| 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/2402.15583 |
| publication_date | 2024-02-23 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 84, 96, 164, 200 |
| abstract_inverted_index.2D | 37 |
| abstract_inverted_index.3D | 33 |
| abstract_inverted_index.In | 122 |
| abstract_inverted_index.To | 79 |
| abstract_inverted_index.We | 181 |
| abstract_inverted_index.as | 146 |
| abstract_inverted_index.at | 75, 171 |
| abstract_inverted_index.by | 72, 185 |
| abstract_inverted_index.if | 40 |
| abstract_inverted_index.in | 7, 22, 47, 63, 95, 104, 112, 119, 203 |
| abstract_inverted_index.of | 4, 16, 32, 55, 68, 151, 178 |
| abstract_inverted_index.on | 190 |
| abstract_inverted_index.to | 1, 60, 90, 101, 135 |
| abstract_inverted_index.we | 41, 82 |
| abstract_inverted_index.Due | 0 |
| abstract_inverted_index.The | 108 |
| abstract_inverted_index.and | 20, 66, 106, 195, 207 |
| abstract_inverted_index.are | 11, 133 |
| abstract_inverted_index.but | 174 |
| abstract_inverted_index.can | 42 |
| abstract_inverted_index.for | 13, 141, 148, 167 |
| abstract_inverted_index.our | 183 |
| abstract_inverted_index.raw | 127 |
| abstract_inverted_index.the | 2, 14, 30, 44, 52, 64, 73, 102, 123, 126, 137, 149, 155, 168, 187 |
| abstract_inverted_index.aids | 111 |
| abstract_inverted_index.both | 204 |
| abstract_inverted_index.cues | 6 |
| abstract_inverted_index.data | 205 |
| abstract_inverted_index.each | 69, 142 |
| abstract_inverted_index.end, | 81 |
| abstract_inverted_index.from | 26, 36, 130, 154 |
| abstract_inverted_index.lack | 3 |
| abstract_inverted_index.map. | 161 |
| abstract_inverted_index.same | 45, 169 |
| abstract_inverted_index.show | 199 |
| abstract_inverted_index.task | 208 |
| abstract_inverted_index.this | 80 |
| abstract_inverted_index.time | 77 |
| abstract_inverted_index.(BEV) | 159 |
| abstract_inverted_index.LiDAR | 131 |
| abstract_inverted_index.depth | 5 |
| abstract_inverted_index.image | 38 |
| abstract_inverted_index.input | 49, 98, 117 |
| abstract_inverted_index.leads | 59 |
| abstract_inverted_index.learn | 91 |
| abstract_inverted_index.model | 189 |
| abstract_inverted_index.novel | 85 |
| abstract_inverted_index.point | 128 |
| abstract_inverted_index.shape | 67 |
| abstract_inverted_index.which | 144 |
| abstract_inverted_index.across | 115 |
| abstract_inverted_index.angles | 28 |
| abstract_inverted_index.bird's | 157 |
| abstract_inverted_index.camera | 74 |
| abstract_inverted_index.change | 103 |
| abstract_inverted_index.clouds | 129 |
| abstract_inverted_index.enable | 29 |
| abstract_inverted_index.frames | 118, 173 |
| abstract_inverted_index.inputs | 10, 39 |
| abstract_inverted_index.nature | 54 |
| abstract_inverted_index.object | 34 |
| abstract_inverted_index.robust | 100 |
| abstract_inverted_index.scenes | 58 |
| abstract_inverted_index.serves | 145 |
| abstract_inverted_index.stage, | 125 |
| abstract_inverted_index.states | 35 |
| abstract_inverted_index.steps. | 78 |
| abstract_inverted_index.tasks. | 121, 197 |
| abstract_inverted_index.Results | 198 |
| abstract_inverted_index.between | 176 |
| abstract_inverted_index.changes | 62 |
| abstract_inverted_index.driving | 57 |
| abstract_inverted_index.dynamic | 53 |
| abstract_inverted_index.feature | 160 |
| abstract_inverted_index.frames. | 50 |
| abstract_inverted_index.images, | 8 |
| abstract_inverted_index.learned | 109 |
| abstract_inverted_index.notable | 201 |
| abstract_inverted_index.propose | 83 |
| abstract_inverted_index.sensors | 132 |
| abstract_inverted_index.success | 15 |
| abstract_inverted_index.various | 191 |
| abstract_inverted_index.Cohere3D | 162 |
| abstract_inverted_index.However, | 51 |
| abstract_inverted_index.captured | 71 |
| abstract_inverted_index.coherent | 92 |
| abstract_inverted_index.distance | 105 |
| abstract_inverted_index.driving. | 24 |
| abstract_inverted_index.evaluate | 182 |
| abstract_inverted_index.eye-view | 158 |
| abstract_inverted_index.guidance | 147 |
| abstract_inverted_index.identify | 43 |
| abstract_inverted_index.instance | 46, 70, 93, 170 |
| abstract_inverted_index.learning | 87 |
| abstract_inverted_index.multiple | 116 |
| abstract_inverted_index.planning | 21, 196 |
| abstract_inverted_index.recovery | 31 |
| abstract_inverted_index.sequence | 99 |
| abstract_inverted_index.temporal | 139 |
| abstract_inverted_index.utilized | 134 |
| abstract_inverted_index.Cohere3D, | 89 |
| abstract_inverted_index.algorithm | 184 |
| abstract_inverted_index.construct | 136 |
| abstract_inverted_index.different | 27, 48, 76, 172, 179 |
| abstract_inverted_index.important | 12 |
| abstract_inverted_index.instance, | 143 |
| abstract_inverted_index.long-term | 97, 138 |
| abstract_inverted_index.algorithm, | 88 |
| abstract_inverted_index.appearance | 65 |
| abstract_inverted_index.autonomous | 23, 56 |
| abstract_inverted_index.consistent | 165 |
| abstract_inverted_index.downstream | 120, 192 |
| abstract_inverted_index.efficiency | 206 |
| abstract_inverted_index.encourages | 163 |
| abstract_inverted_index.extraction | 150 |
| abstract_inverted_index.finetuning | 186 |
| abstract_inverted_index.instances. | 180 |
| abstract_inverted_index.pretrained | 188 |
| abstract_inverted_index.contrastive | 86 |
| abstract_inverted_index.improvement | 202 |
| abstract_inverted_index.multi-frame | 9 |
| abstract_inverted_index.perception, | 18, 193 |
| abstract_inverted_index.prediction, | 19, 194 |
| abstract_inverted_index.pretraining | 124 |
| abstract_inverted_index.significant | 61 |
| abstract_inverted_index.Observations | 25 |
| abstract_inverted_index.performance. | 209 |
| abstract_inverted_index.perspective. | 107 |
| abstract_inverted_index.vision-based | 17, 156 |
| abstract_inverted_index.distinguishes | 175 |
| abstract_inverted_index.correspondence | 114, 140 |
| abstract_inverted_index.instance-level | 113, 152 |
| abstract_inverted_index.representation | 110, 153, 166 |
| abstract_inverted_index.representations | 94, 177 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile |