Lightweight super resolution network for point cloud geometry compression Article Swipe
Wei Zhang
,
Dingquan Li
,
Ge Li
,
Wen Gao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.00970
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.00970
This paper presents an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network. The proposed method involves decomposing a point cloud into a base point cloud and the interpolation patterns for reconstructing the original point cloud. While the base point cloud can be efficiently compressed using any lossless codec, such as Geometry-based Point Cloud Compression, a distinct strategy is employed for handling the interpolation patterns. Rather than directly compressing the interpolation patterns, a lightweight super-resolution network is utilized to learn this information through overfitting. Subsequently, the network parameter is transmitted to assist in point cloud reconstruction at the decoder side. Notably, our approach differentiates itself from lookup table-based methods, allowing us to obtain more accurate interpolation patterns by accessing a broader range of neighboring voxels at an acceptable computational cost. Experiments on MPEG Cat1 (Solid) and Cat2 datasets demonstrate the remarkable compression performance achieved by our method.
Related Topics
Concepts
Point cloud
Computer science
Interpolation (computer graphics)
Lossless compression
Codec
Algorithm
Point (geometry)
Cloud computing
Compression (physics)
Overfitting
Data compression
Geometry
Computational science
Computer vision
Artificial intelligence
Mathematics
Image (mathematics)
Artificial neural network
Telecommunications
Materials science
Composite material
Operating system
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.00970
- https://arxiv.org/pdf/2311.00970
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388328966
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4388328966Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2311.00970Digital Object Identifier
- Title
-
Lightweight super resolution network for point cloud geometry compressionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-02Full publication date if available
- Authors
-
Wei Zhang, Dingquan Li, Ge Li, Wen GaoList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.00970Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2311.00970Direct 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/2311.00970Direct OA link when available
- Concepts
-
Point cloud, Computer science, Interpolation (computer graphics), Lossless compression, Codec, Algorithm, Point (geometry), Cloud computing, Compression (physics), Overfitting, Data compression, Geometry, Computational science, Computer vision, Artificial intelligence, Mathematics, Image (mathematics), Artificial neural network, Telecommunications, Materials science, Composite material, Operating systemTop 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/W4388328966 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2311.00970 |
| ids.doi | https://doi.org/10.48550/arxiv.2311.00970 |
| ids.openalex | https://openalex.org/W4388328966 |
| fwci | |
| type | preprint |
| title | Lightweight super resolution network for point cloud geometry compression |
| 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 | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/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/T10638 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9984999895095825 |
| 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 | Optical measurement and interference techniques |
| topics[2].id | https://openalex.org/T11105 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9925000071525574 |
| 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 | Advanced Image Processing Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C131979681 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8354849815368652 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1899648 |
| concepts[0].display_name | Point cloud |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.7351158857345581 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C137800194 |
| concepts[2].level | 3 |
| concepts[2].score | 0.7313219308853149 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11713455 |
| concepts[2].display_name | Interpolation (computer graphics) |
| concepts[3].id | https://openalex.org/C81081738 |
| concepts[3].level | 3 |
| concepts[3].score | 0.5926293134689331 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q55542 |
| concepts[3].display_name | Lossless compression |
| concepts[4].id | https://openalex.org/C161765866 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5865813493728638 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q184748 |
| concepts[4].display_name | Codec |
| concepts[5].id | https://openalex.org/C11413529 |
| concepts[5].level | 1 |
| concepts[5].score | 0.5398759841918945 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[5].display_name | Algorithm |
| concepts[6].id | https://openalex.org/C28719098 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5056910514831543 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q44946 |
| concepts[6].display_name | Point (geometry) |
| concepts[7].id | https://openalex.org/C79974875 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4960115849971771 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q483639 |
| concepts[7].display_name | Cloud computing |
| concepts[8].id | https://openalex.org/C180016635 |
| concepts[8].level | 2 |
| concepts[8].score | 0.47435081005096436 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2712821 |
| concepts[8].display_name | Compression (physics) |
| concepts[9].id | https://openalex.org/C22019652 |
| concepts[9].level | 3 |
| concepts[9].score | 0.46806564927101135 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q331309 |
| concepts[9].display_name | Overfitting |
| concepts[10].id | https://openalex.org/C78548338 |
| concepts[10].level | 2 |
| concepts[10].score | 0.3580728769302368 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2493 |
| concepts[10].display_name | Data compression |
| concepts[11].id | https://openalex.org/C2524010 |
| concepts[11].level | 1 |
| concepts[11].score | 0.343944251537323 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[11].display_name | Geometry |
| concepts[12].id | https://openalex.org/C459310 |
| concepts[12].level | 1 |
| concepts[12].score | 0.33370548486709595 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q117801 |
| concepts[12].display_name | Computational science |
| concepts[13].id | https://openalex.org/C31972630 |
| concepts[13].level | 1 |
| concepts[13].score | 0.2956269383430481 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q844240 |
| concepts[13].display_name | Computer vision |
| concepts[14].id | https://openalex.org/C154945302 |
| concepts[14].level | 1 |
| concepts[14].score | 0.28458455204963684 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[14].display_name | Artificial intelligence |
| concepts[15].id | https://openalex.org/C33923547 |
| concepts[15].level | 0 |
| concepts[15].score | 0.1676551103591919 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[15].display_name | Mathematics |
| concepts[16].id | https://openalex.org/C115961682 |
| concepts[16].level | 2 |
| concepts[16].score | 0.1273868978023529 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q860623 |
| concepts[16].display_name | Image (mathematics) |
| concepts[17].id | https://openalex.org/C50644808 |
| concepts[17].level | 2 |
| concepts[17].score | 0.09670153260231018 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[17].display_name | Artificial neural network |
| concepts[18].id | https://openalex.org/C76155785 |
| concepts[18].level | 1 |
| concepts[18].score | 0.07943010330200195 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[18].display_name | Telecommunications |
| concepts[19].id | https://openalex.org/C192562407 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q228736 |
| concepts[19].display_name | Materials science |
| concepts[20].id | https://openalex.org/C159985019 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q181790 |
| concepts[20].display_name | Composite material |
| concepts[21].id | https://openalex.org/C111919701 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[21].display_name | Operating system |
| keywords[0].id | https://openalex.org/keywords/point-cloud |
| keywords[0].score | 0.8354849815368652 |
| keywords[0].display_name | Point cloud |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.7351158857345581 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/interpolation |
| keywords[2].score | 0.7313219308853149 |
| keywords[2].display_name | Interpolation (computer graphics) |
| keywords[3].id | https://openalex.org/keywords/lossless-compression |
| keywords[3].score | 0.5926293134689331 |
| keywords[3].display_name | Lossless compression |
| keywords[4].id | https://openalex.org/keywords/codec |
| keywords[4].score | 0.5865813493728638 |
| keywords[4].display_name | Codec |
| keywords[5].id | https://openalex.org/keywords/algorithm |
| keywords[5].score | 0.5398759841918945 |
| keywords[5].display_name | Algorithm |
| keywords[6].id | https://openalex.org/keywords/point |
| keywords[6].score | 0.5056910514831543 |
| keywords[6].display_name | Point (geometry) |
| keywords[7].id | https://openalex.org/keywords/cloud-computing |
| keywords[7].score | 0.4960115849971771 |
| keywords[7].display_name | Cloud computing |
| keywords[8].id | https://openalex.org/keywords/compression |
| keywords[8].score | 0.47435081005096436 |
| keywords[8].display_name | Compression (physics) |
| keywords[9].id | https://openalex.org/keywords/overfitting |
| keywords[9].score | 0.46806564927101135 |
| keywords[9].display_name | Overfitting |
| keywords[10].id | https://openalex.org/keywords/data-compression |
| keywords[10].score | 0.3580728769302368 |
| keywords[10].display_name | Data compression |
| keywords[11].id | https://openalex.org/keywords/geometry |
| keywords[11].score | 0.343944251537323 |
| keywords[11].display_name | Geometry |
| keywords[12].id | https://openalex.org/keywords/computational-science |
| keywords[12].score | 0.33370548486709595 |
| keywords[12].display_name | Computational science |
| keywords[13].id | https://openalex.org/keywords/computer-vision |
| keywords[13].score | 0.2956269383430481 |
| keywords[13].display_name | Computer vision |
| keywords[14].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[14].score | 0.28458455204963684 |
| keywords[14].display_name | Artificial intelligence |
| keywords[15].id | https://openalex.org/keywords/mathematics |
| keywords[15].score | 0.1676551103591919 |
| keywords[15].display_name | Mathematics |
| keywords[16].id | https://openalex.org/keywords/image |
| keywords[16].score | 0.1273868978023529 |
| keywords[16].display_name | Image (mathematics) |
| keywords[17].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[17].score | 0.09670153260231018 |
| keywords[17].display_name | Artificial neural network |
| keywords[18].id | https://openalex.org/keywords/telecommunications |
| keywords[18].score | 0.07943010330200195 |
| keywords[18].display_name | Telecommunications |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2311.00970 |
| 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 | cc-by |
| locations[0].pdf_url | https://arxiv.org/pdf/2311.00970 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2311.00970 |
| locations[1].id | doi:10.48550/arxiv.2311.00970 |
| 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.2311.00970 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5090668308 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-5802-9488 |
| authorships[0].author.display_name | Wei Zhang |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Zhang, Wei |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5029895185 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5549-9027 |
| authorships[1].author.display_name | Dingquan Li |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Li, Dingquan |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5100447691 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-0140-0949 |
| authorships[2].author.display_name | Ge Li |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Li, Ge |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5018478553 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-8070-802X |
| authorships[3].author.display_name | Wen Gao |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Gao, Wen |
| authorships[3].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2311.00970 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-11-04T00:00:00 |
| display_name | Lightweight super resolution network for point cloud geometry compression |
| 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 | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Advanced Vision and Imaging |
| related_works | https://openalex.org/W2948148442, https://openalex.org/W2461250372, https://openalex.org/W2394342941, https://openalex.org/W2169853506, https://openalex.org/W2547124190, https://openalex.org/W2350586049, https://openalex.org/W2385628723, https://openalex.org/W2057878850, https://openalex.org/W2169871401, https://openalex.org/W3008492011 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2311.00970 |
| 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 | cc-by |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2311.00970 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| 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/2311.00970 |
| primary_location.id | pmh:oai:arXiv.org:2311.00970 |
| 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 | cc-by |
| primary_location.pdf_url | https://arxiv.org/pdf/2311.00970 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2311.00970 |
| publication_date | 2023-11-02 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 12, 21, 25, 58, 75, 122 |
| abstract_inverted_index.an | 3, 129 |
| abstract_inverted_index.as | 53 |
| abstract_inverted_index.at | 99, 128 |
| abstract_inverted_index.be | 45 |
| abstract_inverted_index.by | 10, 120, 147 |
| abstract_inverted_index.in | 95 |
| abstract_inverted_index.is | 61, 79, 91 |
| abstract_inverted_index.of | 125 |
| abstract_inverted_index.on | 134 |
| abstract_inverted_index.to | 81, 93, 114 |
| abstract_inverted_index.us | 113 |
| abstract_inverted_index.The | 16 |
| abstract_inverted_index.and | 29, 138 |
| abstract_inverted_index.any | 49 |
| abstract_inverted_index.can | 44 |
| abstract_inverted_index.for | 5, 33, 63 |
| abstract_inverted_index.our | 104, 148 |
| abstract_inverted_index.the | 30, 35, 40, 65, 72, 88, 100, 142 |
| abstract_inverted_index.Cat1 | 136 |
| abstract_inverted_index.Cat2 | 139 |
| abstract_inverted_index.MPEG | 135 |
| abstract_inverted_index.This | 0 |
| abstract_inverted_index.base | 26, 41 |
| abstract_inverted_index.from | 108 |
| abstract_inverted_index.into | 24 |
| abstract_inverted_index.more | 116 |
| abstract_inverted_index.such | 52 |
| abstract_inverted_index.than | 69 |
| abstract_inverted_index.this | 83 |
| abstract_inverted_index.Cloud | 56 |
| abstract_inverted_index.Point | 55 |
| abstract_inverted_index.While | 39 |
| abstract_inverted_index.cloud | 8, 23, 28, 43, 97 |
| abstract_inverted_index.cost. | 132 |
| abstract_inverted_index.learn | 82 |
| abstract_inverted_index.paper | 1 |
| abstract_inverted_index.point | 7, 22, 27, 37, 42, 96 |
| abstract_inverted_index.range | 124 |
| abstract_inverted_index.side. | 102 |
| abstract_inverted_index.using | 48 |
| abstract_inverted_index.Rather | 68 |
| abstract_inverted_index.assist | 94 |
| abstract_inverted_index.cloud. | 38 |
| abstract_inverted_index.codec, | 51 |
| abstract_inverted_index.itself | 107 |
| abstract_inverted_index.lookup | 109 |
| abstract_inverted_index.method | 18 |
| abstract_inverted_index.obtain | 115 |
| abstract_inverted_index.voxels | 127 |
| abstract_inverted_index.(Solid) | 137 |
| abstract_inverted_index.broader | 123 |
| abstract_inverted_index.decoder | 101 |
| abstract_inverted_index.method. | 149 |
| abstract_inverted_index.network | 78, 89 |
| abstract_inverted_index.through | 85 |
| abstract_inverted_index.Notably, | 103 |
| abstract_inverted_index.accurate | 117 |
| abstract_inverted_index.achieved | 146 |
| abstract_inverted_index.allowing | 112 |
| abstract_inverted_index.approach | 4, 105 |
| abstract_inverted_index.datasets | 140 |
| abstract_inverted_index.directly | 70 |
| abstract_inverted_index.distinct | 59 |
| abstract_inverted_index.employed | 62 |
| abstract_inverted_index.geometry | 9 |
| abstract_inverted_index.handling | 64 |
| abstract_inverted_index.involves | 19 |
| abstract_inverted_index.lossless | 50 |
| abstract_inverted_index.methods, | 111 |
| abstract_inverted_index.network. | 15 |
| abstract_inverted_index.original | 36 |
| abstract_inverted_index.patterns | 32, 119 |
| abstract_inverted_index.presents | 2 |
| abstract_inverted_index.proposed | 17 |
| abstract_inverted_index.strategy | 60 |
| abstract_inverted_index.utilized | 80 |
| abstract_inverted_index.accessing | 121 |
| abstract_inverted_index.parameter | 90 |
| abstract_inverted_index.patterns, | 74 |
| abstract_inverted_index.patterns. | 67 |
| abstract_inverted_index.acceptable | 130 |
| abstract_inverted_index.compressed | 47 |
| abstract_inverted_index.leveraging | 11 |
| abstract_inverted_index.remarkable | 143 |
| abstract_inverted_index.Experiments | 133 |
| abstract_inverted_index.compressing | 6, 71 |
| abstract_inverted_index.compression | 144 |
| abstract_inverted_index.decomposing | 20 |
| abstract_inverted_index.demonstrate | 141 |
| abstract_inverted_index.efficiently | 46 |
| abstract_inverted_index.information | 84 |
| abstract_inverted_index.lightweight | 13, 76 |
| abstract_inverted_index.neighboring | 126 |
| abstract_inverted_index.performance | 145 |
| abstract_inverted_index.table-based | 110 |
| abstract_inverted_index.transmitted | 92 |
| abstract_inverted_index.Compression, | 57 |
| abstract_inverted_index.overfitting. | 86 |
| abstract_inverted_index.Subsequently, | 87 |
| abstract_inverted_index.computational | 131 |
| abstract_inverted_index.interpolation | 31, 66, 73, 118 |
| abstract_inverted_index.Geometry-based | 54 |
| abstract_inverted_index.differentiates | 106 |
| abstract_inverted_index.reconstructing | 34 |
| abstract_inverted_index.reconstruction | 98 |
| abstract_inverted_index.super-resolution | 14, 77 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |