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This is the repository for the NBMOD dataset and the code of the paper "NBMOD: Find It and Grasp It in Noisy Background.
TypeChat is a library that makes it easy to build natural language interfaces using types.
[ICLR24] Official PyTorch Implementation of Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors
Generative Grasping CNN from "Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach" (RSS 2018)
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Gated Channel Transformation for Visual Recognition (CVPR 2020)
ICRA 2022 "Hybrid Physical Metric For 6-DoF Grasp Pose Detection"
本项目收录约30个模型，皆为pybullet适用的urdf文件，模型大小被缩放到了相近的大小，同时，这些模型的选择主要是参考如cocos、imagenet数据集中的类别，并且广泛应用于yolo、mask2former、detr等目标检测项目中。经过测试，大部分urdf模型可以被这些开源网络识别到。
Pytorch implementation of diffusion models on Lie Groups for 6D grasp pose generation https://sites.google.com/view/se3dif/home
Collection of object models compatible with pybullet simulator https://github.com/bulletphysics/bullet3/tree/master/examples/pybullet
Code for CoRL2022 paper "Towards Scale Balanced 6-DoF Grasp Detection in Cluttered Scenes"
paper list of robotic grasping and some related works
PointNetGPD is an end-to-end grasp evaluation model to address the challenging problem of localizing robot grasp configurations directly from the point cloud.
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This repository contains a sample of the grasping dataset and tools to visualize grasps, generate random scenes, and render observations. The two sample files are in the HDF5 format.
[ICRA 2023 & IROS 2023] Code release for Keypoint-GraspNet (KGN) and Keypoint-GraspNet-V2 (KGNv2)
OpenMMLab Detection Toolbox and Benchmark
This repo holds the official code and data for "Unveiling Parts Beyond Objects: Towards Finer-Granularity Referring Expression Segmentation", accepted by CVPR 2024.
This repository is for the first comprehensive survey on Meta AI's Segment Anything Model (SAM).
My implementation of Graspnet Graspness.
Official code of paper "Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes"
Code for ICRA21 paper "End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB".