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CAD Model Multi-Object (CAMO)
The CAMO dataset contains 3D point clouds of objects from CAD models. -
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
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. -
ModelNet dataset
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. -
PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer
Recent Transformer-based 3D object detectors learn point cloud features either from point- or voxel-based representations. -
Shapenets: A Deep Representation for Volumetric Shapes
3D shapes are represented as point clouds, which are unordered sets of 3D points. -
McGill 3D dataset
The McGill 3D dataset used in this paper for testing the generalization of the proposed occupancy learning method. -
8i Voxelized Full Bodies
Point clouds are 3D point sets containing both geometry coordinates and associated attributes, which can describe scenes correctly and stereoscopically. Recently, point clouds... -
Microsoft Voxelized Upper Bodies
Point clouds are 3D point sets containing both geometry coordinates and associated attributes, which can describe scenes correctly and stereoscopically. Recently, point clouds... -
Antholzer et al. dataset
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. -
Shapenet dataset
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
Object re-identification from point clouds -
One Million Scenes for Autonomous Driving
The ONCE dataset is a large-scale dataset for autonomous driving, containing 581 sequences composed of 20 labeled frames and 561 unlabeled frames. -
TORCHSPARSE: EFFICIENT POINT CLOUD INFERENCE ENGINE
Deep learning on point clouds has received increased attention thanks to its wide applications in AR/VR and autonomous driving. -
TUM-MLS-2016
TUM-MLS-2016: An annotated mobile LiDAR dataset of the TUM City Campus for semantic point cloud interpretation in urban areas. -
ScanNet and ArkitScenes
The dataset used in the Point2Pix paper, containing point clouds and camera parameters for indoor scenes. -
ScanNet-v2
Learning from bounding-boxes annotations has shown great potential in weakly-supervised 3D point cloud instance segmentation. However, we observed that existing methods would...
