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