ABO: Dataset and Benchmarks for Real-World 3D Object Understanding
- URL: http://arxiv.org/abs/2110.06199v1
- Date: Tue, 12 Oct 2021 17:52:42 GMT
- Title: ABO: Dataset and Benchmarks for Real-World 3D Object Understanding
- Authors: Jasmine Collins, Shubham Goel, Achleshwar Luthra, Leon Xu, Kenan Deng,
Xi Zhang, Tomas F. Yago Vicente, Himanshu Arora, Thomas Dideriksen, Matthieu
Guillaumin, Jitendra Malik
- Abstract summary: Amazon-Berkeley Objects (ABO) is a large-scale dataset of product images and 3D models corresponding to real household objects.
We use ABO to measure the domain gap for single-view 3D reconstruction networks trained on synthetic objects.
We also use multi-view images from ABO to measure the robustness of state-of-the-art metric learning approaches to different camera viewpoints.
- Score: 43.42504014918771
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce Amazon-Berkeley Objects (ABO), a new large-scale dataset of
product images and 3D models corresponding to real household objects. We use
this realistic, object-centric 3D dataset to measure the domain gap for
single-view 3D reconstruction networks trained on synthetic objects. We also
use multi-view images from ABO to measure the robustness of state-of-the-art
metric learning approaches to different camera viewpoints. Finally, leveraging
the physically-based rendering materials in ABO, we perform single- and
multi-view material estimation for a variety of complex, real-world geometries.
The full dataset is available for download at
https://amazon-berkeley-objects.s3.amazonaws.com/index.html.
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