Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/40304
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DC Field | Value | Language |
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dc.date.accessioned | 2023-06-05T11:48:36Z | - |
dc.date.available | 2023-06-05T11:48:36Z | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2023-01-16T09:57:09Z | - |
dc.identifier.citation | Github. https://pderoovere.github.io/dimo/ | - |
dc.identifier.uri | http://hdl.handle.net/1942/40304 | - |
dc.description.abstract | We present a diverse dataset of industrial metal objects. These objects are symmetric, textureless and highly reflective, leading to challenging conditions not captured in existing datasets. Our 6D object pose estimation dataset contains both real-world and synthetic images. Real-world data is obtained by recording multi-view images of scenes with varying object shapes, materials, carriers, compositions and lighting conditions. This leads to over 30,000 images, accurately labelled using a new public tool. Synthetic data is obtained by carefully simulating real-world conditions and varying them in a controlled and realistic way. This leads to over 500,000 synthetic images. The close correspondence between synthetic and real-world data, and controlled variations, will facilitate sim-to-real research. Our dataset's size and challenging nature will facilitate research on various computer vision tasks involving reflective materials. | - |
dc.language.iso | en | - |
dc.publisher | Github | - |
dc.subject.classification | Computer vision | - |
dc.subject.classification | Computer graphics | - |
dc.subject.other | Synthetic Data | - |
dc.subject.other | Machine Learning | - |
dc.subject.other | Computer Vision | - |
dc.subject.other | SIM2REAL | - |
dc.subject.other | Graphics | - |
dc.subject.other | Deep learning | - |
dc.subject.other | digital twin | - |
dc.title | Dataset of Industrial Metal Objects | - |
dc.type | Dataset | - |
local.bibliographicCitation.jcat | DS | - |
dc.rights.license | Creative Commons Attribution 4.0 International (CC-BY-4.0) | - |
dc.identifier.url | https://pderoovere.github.io/dimo/ | - |
dc.description.other | We provide dimo_loader.py to easily load the dataset. This file is available on GitHub. The dataset is structured using the BOP dataset format, with two additional files: scene_gt_world.json with object poses in a global world frame. scene_info.json with information about the data-generation process (which light / carrier / parts configuration). | - |
local.dataset.url | https://pderoovere.github.io/dimo/ | - |
local.uhasselt.international | no | - |
local.contributor.datacreator | De Roovere, Peter | - |
local.contributor.datacreator | MOONEN, Steven | - |
local.contributor.datacreator | MICHIELS, Nick | - |
local.contributor.datacreator | Wyffels, Francis | - |
local.contributor.datacurator | De Roovere, Peter | - |
local.contributor.datacurator | MOONEN, Steven | - |
local.contributor.rightsholder | De Roovere, Peter | - |
local.contributor.rightsholder | MOONEN, Steven | - |
local.format.extent | small: 64GB, base: 11.6MB, all: 2.5TB | - |
local.format.mimetype | BOP | - |
local.format.mimetype | json | - |
local.contributororcid.datacreator | 0000-0002-0935-701X | - |
local.contributororcid.datacreator | 0000-0002-7047-5867 | - |
local.contributororcid.datacreator | 0000-0002-5491-8349 | - |
local.contributororcid.datacurator | 0000-0002-0935-701X | - |
local.contributororcid.rightsholder | 0000-0002-0935-701X | - |
local.publication.doi | 10.48550/arXiv.2208.04052 | - |
local.contributingorg.datacreator | Ghent University | - |
local.contributingorg.datacreator | Hasselt University | - |
local.contributingorg.datacurator | Ghent University | - |
local.contributingorg.datacurator | Hasselt University | - |
local.contributingorg.rightsholder | Ghent University | - |
local.contributingorg.rightsholder | Hasselt University | - |
dc.rights.access | Open Access | - |
item.fulltext | No Fulltext | - |
item.fullcitation | De Roovere, Peter; MOONEN, Steven; MICHIELS, Nick & Wyffels, Francis (2022) Dataset of Industrial Metal Objects. Github. https://pderoovere.github.io/dimo/. | - |
item.accessRights | Closed Access | - |
item.contributor | De Roovere, Peter | - |
item.contributor | MOONEN, Steven | - |
item.contributor | MICHIELS, Nick | - |
item.contributor | Wyffels, Francis | - |
crisitem.license.code | CC-BY-4.0 | - |
crisitem.license.name | Creative Commons Attribution 4.0 International (CC-BY-4.0) | - |
crisitem.discipline.code | 01020902 | - |
crisitem.discipline.code | 01020901 | - |
crisitem.discipline.name | Computer vision | - |
crisitem.discipline.name | Computer graphics | - |
crisitem.discipline.path | Natural sciences > Information and computing sciences > Visual computing > Computer vision | - |
crisitem.discipline.path | Natural sciences > Information and computing sciences > Visual computing > Computer graphics | - |
crisitem.discipline.pathandcode | Natural sciences > Information and computing sciences > Visual computing > Computer vision (01020902) | - |
crisitem.discipline.pathandcode | Natural sciences > Information and computing sciences > Visual computing > Computer graphics (01020901) | - |
Appears in Collections: | Datasets |
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