12 datasets found

A multi-stage point completion network for 3D object reconstruction.

A point cloud completion network for 3D object reconstruction from a single image.

Point cloud completion aims to recover the complete shape based on a partial observation. The proposed Partial2Complete (P2C) framework completes point cloud objects using...

The 3D-EPN dataset is a point cloud completion benchmark derived from the ShapeNet dataset.

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 Pacific Graphics 2022 dataset is a collection of 3D models used for point cloud completion tasks.

ShapeNet-Part dataset consists of 16881 3D objects, covering 16 shape categories. Most of the point cloud instances are annotated with less than six part labels, and there exist...

A variational relational point completion network that generates complete point clouds from partial point clouds.

A point cloud completion network that preserves the observed shape details and generates complete point clouds.

A point cloud completion network that generates complete 3D shapes from partial point clouds.

The Point Completion Network (PCN) dataset is a large-scale, multi-category dataset for shape completion. It contains pairs of partial and complete point clouds, where the...

A large-scale Multi-View Partial point cloud dataset, which consists of over 100,000 high-quality incomplete and complete point clouds.