Objaverse-XL: A Universe of 10M+ 3D Objects
Matt Deitke Ruoshi Liu Matthew Wallingford Huong Ngo Oscar Michel Aditya Kusupati Alan Fan Christian Laforte Vikram Voleti Samir Yitzhak Gadre Eli VanderBilt Aniruddha Kembhavi Carl Vondrick Georgia Gkioxari Kiana Ehsani Ludwig Schmidt Ali Farhadi
Natural language processing and 2D vision models have attained remarkable
proficiency on many tasks primarily by escalating the scale of training data.
However, 3D vision tasks have not seen the same progress, in part due to the
challenges of acquiring high-quality 3D data. In this work, we present
Objaverse-XL, a dataset of over 10 million 3D objects. Our dataset comprises
deduplicated 3D objects from a diverse set of sources, including manually
designed objects, photogrammetry scans of landmarks and everyday items, and
professional scans of historic and antique artifacts. Representing the largest
scale and diversity in the realm of 3D datasets, Objaverse-XL enables
significant new possibilities for 3D vision. Our experiments demonstrate the
improvements enabled with the scale provided by Objaverse-XL. We show that by
training Zero123 on novel view synthesis, utilizing over 100 million multi-view
rendered images, we achieve strong zero-shot generalization abilities. We hope
that releasing Objaverse-XL will enable further innovations in the field of 3D
vision at scale.