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 complex geometries and physically-based materials that correspond 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.
@article{arxiv.2110.06199,
title = {ABO: Dataset and Benchmarks for Real-World 3D Object Understanding},
author = {Jasmine Collins and Shubham Goel and Kenan Deng and Achleshwar Luthra and Leon Xu and Erhan Gundogdu and Xi Zhang and Tomas F. Yago Vicente and Thomas Dideriksen and Himanshu Arora and Matthieu Guillaumin and Jitendra Malik},
journal= {arXiv preprint arXiv:2110.06199},
year = {2022}
}