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6D pose estimation is performed on the object, and then the robot is controlled to grasp the object.
6D pose estimation
Recent papers about generalizable 6DoF object pose estimation.
This repository is a curated list of papers and open source code for 6D Object Pose Estimation.
CamOdoCal: Automatic Intrinsic and Extrinsic Calibration of a Rig with Multiple Generic Cameras and Odometry
Convolutional Spatial Propagation Network
Deep Object Pose Estimation (DOPE) – ROS inference (CoRL 2018)
"DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion" code repository
Dense Receptive Field For Object Detection
Simple, straighforward ROS library for hand-eye calibration
[CVPR2021 Oral] FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation.
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Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
Gluon-Mobilenet-yolov3
Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020)
Toolbox for our GraspNet-1Billion dataset.
Easy to use and accurate hand eye calibration which has been working reliably for years (2016-present) with kinect, kinectv2, rgbd cameras, optical trackers, and several robots including the ur5 and kuka iiwa.
Implementation of HybridPose: 6D Object Pose Estimation under Hybrid Representation
关于Python的面试题
LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes
LatentFusion: End-to-End Differentiable Reconstruction and Rendering for Unseen Object Pose Estimation
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Open MMLab Detection Toolbox and Benchmark
Models and examples built with TensorFlow
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation on Python3, Tensorflow, and Keras
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