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  5. ABO: Dataset and Benchmarks for Real-World 3D Object Understanding

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Article
en
2022

ABO: Dataset and Benchmarks for Real-World 3D Object Understanding

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en
2022
DOI: 10.1109/cvpr52688.2022.02045

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Jitendra Malik
Jitendra Malik

University of California, Berkeley

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Jasmine Collins
Shubham Goel
Kenan Deng
+9 more

Abstract

We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog images, metadata, and artist-created 3D models with com-plex geometries and physically-based materials that cor-respond to real, household objects. We derive challenging benchmarks that exploit the unique properties of ABO and measure the current limits of the state-of-the-art on three open problems for real-world 3D object understanding: single-view 3D reconstruction, material estimation, and cross-domain multi-view object retrieval.

How to cite this publication

Jasmine Collins, Shubham Goel, Kenan Deng, Achleshwar Luthra, Leon L. Xu, Erhan Gündoğdu, Xi Zhang, Tomás F. Yago Vicente, T.L. Dideriksen, Himanshu Arora, Matthieu Guillaumin, Jitendra Malik (2022). ABO: Dataset and Benchmarks for Real-World 3D Object Understanding. , DOI: https://doi.org/10.1109/cvpr52688.2022.02045.

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Publication Details

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Article

Year

2022

Authors

12

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1109/cvpr52688.2022.02045

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