50 datasets found

Graph-ModelNet40, Graph-ModelNet10, Graph-ShapeNet Part are graph datasets constructed for graph classification task.

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

A dataset for visual grounding on point clouds, focusing on relation modeling.

Self-driving dataset for scene flow estimation

The Waymo Open Dataset and the nuScenes Dataset are used to evaluate the performance of the AFDetV2 model.

The ShapeNet part dataset contains 16,881 shapes from 16 classes and 50 parts in total.

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

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 paper is ScanNetV2, a real-world dataset for indoor scenes. It contains 1205 training scenes and 312 testing scenes, with instance-level object bounding...

SynthCity: A large scale synthetic point cloud dataset for pre-training deep learning models, enabling generalisation and expansion of their usage to real-world data.

The dataset used in the PointCLIP paper is ModelNet10, ModelNet40, and ScanObjectNN. ModelNet10 consists of 4,899 synthetic meshed CAD models with 10 indoor categories, 3,991...

The MPEG 8i dataset contains 6 voxelized point cloud models.

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

The Stanford S3DIS dataset is a large-scale indoor dataset containing point clouds and semantic labels.

The SemanticKITTI dataset is a LiDAR-based semantic segmentation dataset, which consists of 10 sequences for training and 1 for validation.

SEED is a 3D DETR framework for detecting 3D objects from point clouds. It involves a dual query selection module and a deformable grid attention module.

The VoxelNet dataset is a large-scale dataset for 3D object detection, consisting of 3D point clouds and corresponding annotations.

The Multi-View 3D Object Detection Network is a dataset for 3D object detection, consisting of 3D point clouds and corresponding annotations.