Robust Large-Scale Localization in 3D Point Clouds Revisited Article Swipe
Fabian Tschopp
,
Marco Zorzi
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1511.01156
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1511.01156
We tackle the problem of getting a full 6-DOF pose estimation of a query image inside a given point cloud. This technical report re-evaluates the algorithms proposed by Y. Li et al. "Worldwide Pose Estimation using 3D Point Cloud". Our code computes poses from 3 or 4 points, with both known and unknown focal length. The results can easily be displayed and analyzed with Meshlab. We found both advantages and shortcomings of the methods proposed. Furthermore, additional priors and parameters for point selection, RANSAC and pose quality estimate (inlier test) are proposed and applied.
Related Topics
Concepts
RANSAC
Point cloud
Computer science
Scale (ratio)
Point (geometry)
Prior probability
Code (set theory)
Pose
Artificial intelligence
Selection (genetic algorithm)
Computer vision
Robustness (evolution)
Algorithm
Image (mathematics)
Data mining
Mathematics
Geography
Cartography
Chemistry
Programming language
Set (abstract data type)
Geometry
Gene
Bayesian probability
Biochemistry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1511.01156
- https://arxiv.org/pdf/1511.01156
- OA Status
- green
- References
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2224147511
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2224147511Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1511.01156Digital Object Identifier
- Title
-
Robust Large-Scale Localization in 3D Point Clouds RevisitedWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-11-03Full publication date if available
- Authors
-
Fabian Tschopp, Marco ZorziList of authors in order
- Landing page
-
https://arxiv.org/abs/1511.01156Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/1511.01156Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1511.01156Direct OA link when available
- Concepts
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RANSAC, Point cloud, Computer science, Scale (ratio), Point (geometry), Prior probability, Code (set theory), Pose, Artificial intelligence, Selection (genetic algorithm), Computer vision, Robustness (evolution), Algorithm, Image (mathematics), Data mining, Mathematics, Geography, Cartography, Chemistry, Programming language, Set (abstract data type), Geometry, Gene, Bayesian probability, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
5Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.The | 55 |
| abstract_inverted_index.al. | 31 |
| abstract_inverted_index.and | 51, 61, 69, 78, 84, 92 |
| abstract_inverted_index.are | 90 |
| abstract_inverted_index.can | 57 |
| abstract_inverted_index.for | 80 |
| abstract_inverted_index.the | 2, 24, 72 |
| abstract_inverted_index.Pose | 33 |
| abstract_inverted_index.This | 20 |
| abstract_inverted_index.both | 49, 67 |
| abstract_inverted_index.code | 40 |
| abstract_inverted_index.from | 43 |
| abstract_inverted_index.full | 7 |
| abstract_inverted_index.pose | 9, 85 |
| abstract_inverted_index.with | 48, 63 |
| abstract_inverted_index.6-DOF | 8 |
| abstract_inverted_index.Point | 37 |
| abstract_inverted_index.focal | 53 |
| abstract_inverted_index.found | 66 |
| abstract_inverted_index.given | 17 |
| abstract_inverted_index.image | 14 |
| abstract_inverted_index.known | 50 |
| abstract_inverted_index.point | 18, 81 |
| abstract_inverted_index.poses | 42 |
| abstract_inverted_index.query | 13 |
| abstract_inverted_index.test) | 89 |
| abstract_inverted_index.using | 35 |
| abstract_inverted_index.RANSAC | 83 |
| abstract_inverted_index.cloud. | 19 |
| abstract_inverted_index.easily | 58 |
| abstract_inverted_index.inside | 15 |
| abstract_inverted_index.priors | 77 |
| abstract_inverted_index.report | 22 |
| abstract_inverted_index.tackle | 1 |
| abstract_inverted_index.(inlier | 88 |
| abstract_inverted_index.Cloud". | 38 |
| abstract_inverted_index.getting | 5 |
| abstract_inverted_index.length. | 54 |
| abstract_inverted_index.methods | 73 |
| abstract_inverted_index.points, | 47 |
| abstract_inverted_index.problem | 3 |
| abstract_inverted_index.quality | 86 |
| abstract_inverted_index.results | 56 |
| abstract_inverted_index.unknown | 52 |
| abstract_inverted_index.Meshlab. | 64 |
| abstract_inverted_index.analyzed | 62 |
| abstract_inverted_index.applied. | 93 |
| abstract_inverted_index.computes | 41 |
| abstract_inverted_index.estimate | 87 |
| abstract_inverted_index.proposed | 26, 91 |
| abstract_inverted_index.displayed | 60 |
| abstract_inverted_index.proposed. | 74 |
| abstract_inverted_index.technical | 21 |
| abstract_inverted_index."Worldwide | 32 |
| abstract_inverted_index.Estimation | 34 |
| abstract_inverted_index.additional | 76 |
| abstract_inverted_index.advantages | 68 |
| abstract_inverted_index.algorithms | 25 |
| abstract_inverted_index.estimation | 10 |
| abstract_inverted_index.parameters | 79 |
| abstract_inverted_index.selection, | 82 |
| abstract_inverted_index.Furthermore, | 75 |
| abstract_inverted_index.re-evaluates | 23 |
| abstract_inverted_index.shortcomings | 70 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |