33 datasets found

The CAMO dataset contains 3D point clouds of objects from CAD models.

A fast and accurate one-stage approach to visual grounding

Cooperative holistic understanding for visual grounding on point clouds through instance multi-level contextual referring

Free-form description guided 3D visual graph network for 3D object grounding in point clouds

The dataset used in the paper is a collection of point clouds with spinor features, where each point cloud is associated with a spinor feature and a regression target.

The dataset used for surface reconstruction from point clouds using implicit neural representations (INRs) and energy-based models (EBMs).

3D shapes are represented as point clouds, which are unordered sets of 3D points.

The McGill 3D dataset used in this paper for testing the generalization of the proposed occupancy learning method.

A dataset of 3D point clouds of human meshes.

Point clouds are 3D point sets containing both geometry coordinates and associated attributes, which can describe scenes correctly and stereoscopically. Recently, point clouds...

Point clouds are 3D point sets containing both geometry coordinates and associated attributes, which can describe scenes correctly and stereoscopically. Recently, point clouds...

A custom dataset created for this paper, consisting of 2107 point clouds, each with 16384 points, and three design point clouds: stripes, porous, and cut.

A dataset of volumetric point clouds for training an autoencoder for point clouds, consisting of four types of point clouds: encapsulated spheres, encapsulated cuboids,...

The GET3D dataset is a dataset of 3D shapes, consisting of 3,000 models, each described by 2,048 points.

The LION dataset is a dataset of 3D shapes, consisting of 3,000 models, each described by 2,048 points.

Deep learning on point clouds has received increased attention thanks to its wide applications in AR/VR and autonomous driving.

The dataset used in the Point2Pix paper, containing point clouds and camera parameters for indoor scenes.

The dataset used in the paper is a collection of unpaired shapes, including chairs and tables, and letters in different font styles.

The dataset used in the paper is a collection of unpaired shapes, including chairs and tables, and letters in different font styles.

A large-scale point cloud classification benchmark, focusing on semantic segmentation of urban scenes.