35 datasets found

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

Synth4D is a dataset of 20,000 LiDAR point clouds synthesized by the CARLAsimulator.

Point-BERT is a new paradigm for learning point cloud Transformers. It pre-trains standard point cloud Transformers with a Masked Point Modeling (MPM) task.

The dataset used for training a Convolutional Neural Network to predict a grasp quality score over all grasp poses, given a depth image of an object.

Recent Transformer-based 3D object detectors learn point cloud features either from point- or voxel-based representations.

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,...

Object re-identification from point clouds

The ONCE dataset is a large-scale dataset for autonomous driving, containing 581 sequences composed of 20 labeled frames and 561 unlabeled frames.

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

A new 3D dataset, CC3D, a collection of 50k+ aligned pairs of meshes, a CAD model and its virtually 3D scanned counterpart with corresponding scanning artifacts.

MF: A proprietary mobile mapping platform dataset.

TUM-MLS-2016: An annotated mobile LiDAR dataset of the TUM City Campus for semantic point cloud interpretation in urban areas.

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

Learning from bounding-boxes annotations has shown great potential in weakly-supervised 3D point cloud instance segmentation. However, we observed that existing methods would...