MPI3D is a benchmark dataset that consists of 1,036,800 examples of 3D objects from three different domains: Toy, RealisticC, and Real.

Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen...

The Objaverse dataset contains around 800k 3D objects. After adopting simple filter leveraging CLIP [27] to remove the objects whose rendered images are not relevant to its...

A large-scale dataset of 3D objects for training and testing 3D reconstruction models.

Point cloud registration is a crucial problem in computer vision and robotics. Existing methods either rely on matching local geometric features, which are sensitive to the pose...