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dc.contributor.authorKoch, Sebastianen
dc.contributor.authorMatveev, Alberten
dc.contributor.authorJiang, Zhongshien
dc.contributor.authorWilliams, Francisen
dc.contributor.authorArtemov, Alexeyen
dc.contributor.authorBurnaev, Evgenyen
dc.contributor.authorAlexa, Marcen
dc.contributor.authorZorin, Denisen
dc.contributor.authorPanozzo, Danieleen
dc.date.accessioned2019-04-17T09:04:56Z-
dc.date.available2019-04-17T09:04:56Z-
dc.date.issued2019en
dc.identifier.otherhttps://deep-geometry.github.io/abc-dataseten
dc.identifier.urihttp://hdl.handle.net/2451/44326-
dc.descriptionABC Dataset Chunk 0010 (10k objects). The dataset is released under the MIT License. We are grateful to Onshape for providing the CAD models and support. The copyright of these CAD models is owned by their creators. For licensing details, see Onshape Terms of Use 1.g.ii. (https://www.onshape.com/legal/terms-of-use#your_content). This work was supported in part through the NYU IT High Performance Computing resources, services, and staff expertise. Funding provided by NSF award MRI-1229185. We thank the Skoltech CDISE HPC Zhores cluster staff for computing cluster provision. This work was supported in part by NSF CAREER award 1652515, the NSF grants IIS-1320635, DMS-1436591, and 1835712, the Russian Science Foundation under Grant 19-41-04109, and gifts from Adobe Research, nTopology Inc, and NVIDIA. For more information visit the ABC dataset website https://deep-geometry.github.io/abc-dataseten
dc.description.abstractWe introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms. As a use case for our dataset, we perform a large-scale benchmark for estimation of surface normals, comparing existing data driven methods and evaluating their performance against both the ground truth and traditional normal estimation methods.en
dc.description.sponsorshipWe are grateful to Onshape for providing the CAD models and support. The copyright of these CAD models is owned by their creators. For licensing details, see Onshape Terms of Use 1.g.ii. (https://www.onshape.com/legal/terms-of-use#your_content). This work was supported in part through the NYU IT High Performance Computing resources, services, and staff expertise. Funding provided by NSF award MRI-1229185. We thank the Skoltech CDISE HPC Zhores cluster staff for computing cluster provision. This work was supported in part by NSF CAREER award 1652515, the NSF grants IIS-1320635, DMS-1436591, and 1835712, the Russian Science Foundation under Grant 19-41-04109, and gifts from Adobe Research, nTopology Inc, and NVIDIA.en
dc.language.isoenen
dc.relation.ispartofseriesCVPR 2019en
dc.rightsMIT Licenseen
dc.subjectABC Dataseten
dc.subjectCAD Dataseten
dc.subjectGeometric Deep Learningen
dc.subjectNormal Estimationen
dc.titleABC Dataset Chunk 0010en
dc.typeDataseten
Appears in Collections:ABC: A Big CAD Model Dataset For Geometric Deep Learning

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abc_0010_feat_v00.7z3.75 GBUnknownView/Open
abc_0010_meta_v00.7z734.33 kBUnknownView/Open
abc_0010_obj_v00.7z6.6 GBUnknownView/Open
abc_0010_para_v00.7z1.25 GBUnknownView/Open
abc_0010_stat_v00.7z1.51 MBUnknownView/Open
abc_0010_step_v00.7z774.15 MBUnknownView/Open
abc_0010_stl2_v00.7z5.48 GBUnknownView/Open


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