From 43119632544e06c4a77336365ce1479e6abd8da1 Mon Sep 17 00:00:00 2001 From: jwangzzz <j96w@qq.com> Date: Wed, 16 Jan 2019 09:58:09 +0800 Subject: [PATCH] add arXiv link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 60acb30..36bf7b1 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ ## Overview -This repository is the implementation code of the paper "DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion"([arXiv](), [Project](https://sites.google.com/view/densefusion), [Video](https://www.youtube.com/watch?v=SsE5-FuK5jo)) by Chen et al. at [Stanford Vision and Learning Lab](http://svl.stanford.edu/). The model takes an RGB-D image as input and predicts the 6D pose of the each object in the frame. This network is implemented using [PyTorch](https://pytorch.org/) and the rest of the framework is in Python. Since this project focuses on the 6D pose estimation process, we do not specifically limit the choice of the segmentation models. You can choose your preferred semantic-segmentation/instance-segmentation methods according to your needs. In this repo, we provide our full implementation code of the DenseFusion model, Iterative Refinement model and a vanilla SegNet semantic-segmentation model used in our real-robot grasping experiment. The ROS code of the real robot grasping experiment is not included. +This repository is the implementation code of the paper "DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion"([arXiv](https://arxiv.org/abs/1901.04780), [Project](https://sites.google.com/view/densefusion), [Video](https://www.youtube.com/watch?v=SsE5-FuK5jo)) by Chen et al. at [Stanford Vision and Learning Lab](http://svl.stanford.edu/) and [Stanford People, AI & Robots Group](http://pair.stanford.edu/). The model takes an RGB-D image as input and predicts the 6D pose of the each object in the frame. This network is implemented using [PyTorch](https://pytorch.org/) and the rest of the framework is in Python. Since this project focuses on the 6D pose estimation process, we do not specifically limit the choice of the segmentation models. You can choose your preferred semantic-segmentation/instance-segmentation methods according to your needs. In this repo, we provide our full implementation code of the DenseFusion model, Iterative Refinement model and a vanilla SegNet semantic-segmentation model used in our real-robot grasping experiment. The ROS code of the real robot grasping experiment is not included. ## Requirements -- GitLab