grasp
Datasets
All datasets matching “grasp”grasp-datasetGrasp-Any-Region-Dataset
Grasp Any Region Dataset
This repository contains the training dataset for the paper: Grasp Any Region: Towards Precise, Contextual Pixel Understanding for Multimodal LLMs.
Code: https://github.com/Haochen-Wang409/Grasp-Any-Region
About the Dataset
The Grasp Any Region (GAR) dataset is designed to empower Multimodal Large Language Models (MLLMs) with comprehensive region-level visual understanding. While MLLMs excel at holistic understanding, they often struggle with… See the full description on the dataset page: https://huggingface.co/datasets/HaochenWang/Grasp-Any-Region-Dataset.grare-graspnetGrasp-Anything-6D
Grasp-Anything-6D Dataset
We introduce the Grasp-Anything-6D dataset. Building upon our previous Grasp-Anything dataset, Grasp-Anything-6D is a large-scale dataset for the task of language-driven 6-DoF grasp detection.
Our ECCV2024 paper can be accessed at arXiv.
Dataset Structure:
The dataset is structured as follows:
pc: contains .npy files of point cloud scenes. Each scene consists of 8192 points.
pc_mask: contains .npy files of 3D masks for objects in point cloud… See the full description on the dataset page: https://huggingface.co/datasets/airvlab/Grasp-Anything-6D.graspclutter6d
Dataset Card for graspclutter6d
GraspClutter6D is a large-scale real-world dataset for robust perception and grasping in cluttered scenes. This FiftyOne dataset contains a curated subset of 99 scenes from the full GraspClutter6D dataset, optimized for visualization and exploration.
This is a FiftyOne dataset with 111 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from huggingface_hub… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/graspclutter6d.GraspClutter6D
GraspClutter6D Dataset
GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes
Seunghyeok Back, Joosoon Lee, Kangmin Kim, Heeseon Rho, Geonhyup Lee, Raeyoung Kang, Sangbeom Lee, Sangjun Noh, Youngjin Lee, Taeyeop Lee, Kyoobin Lee
Accepted at IEEE Robotics and Automation Letters (RA-L)
[ArXiv] [Project Website] [Video] [Dataset Format] [GitHub]
To download the dataset, run the following commands:mkdir GraspClutter6D && cd GraspClutter6D… See the full description on the dataset page: https://huggingface.co/datasets/GraspClutter6D/GraspClutter6D.