From a083c20a9427335945e997314336b05181f176d8 Mon Sep 17 00:00:00 2001
From: jwangzzz <j96w@qq.com>
Date: Thu, 17 Jan 2019 11:27:44 +0800
Subject: [PATCH] add bib
---
README.md | 13 +++++++++++++
datasets/linemod/dataset.py | 12 ++++++------
datasets/ycb/dataset.py | 12 ++++++------
3 files changed, 25 insertions(+), 12 deletions(-)
diff --git a/README.md b/README.md
index 36bf7b1..6674001 100644
--- a/README.md
+++ b/README.md
@@ -16,6 +16,7 @@
- [Results](#results)
- [Trained Checkpoints](#trained-checkpoints)
- [Tips for your own dataset](#tips-for-your-own-dataset)
+- [Citations](#citations)
- [License](#license)
## Overview
@@ -179,5 +180,17 @@ You can download the trained DenseFusion and Iterative Refinement models of both
## Tips for your own dataset
As you can see in this repo, the network code and the hyperparameters(lr and w) remain the same for both datasets. Which means you might not need to adjust too much on the network structure and hyperparameters when you use this repo on your own dataset. Please make sure that the distance metric in your dataset should be converted to meter, otherwise the hyperparameter w need to be adjusted. Several useful tools including [LabelFusion](https://github.com/RobotLocomotion/LabelFusion) and [sixd_toolkit](https://github.com/thodan/sixd_toolkit) has been tested to work well. (Please make sure to turn on the depth image collection in LabelFusion when you use it.)
+
+## Citations
+Please cite [DenseFusion](https://sites.google.com/view/densefusion) if you use this repository in your publications:
+```
+@article{wang2019densefusion,
+ title={DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion},
+ author={Wang, Chen and Xu, Danfei and Zhu, Yuke and Mart{\'\i}n-Mart{\'\i}n, Roberto and Lu, Cewu and Fei-Fei, Li and Savarese, Silvio},
+ journal={arXiv preprint arXiv:1901.04780},
+ year={2019}
+}
+```
+
## License
Licensed under the [MIT License](LICENSE)
\ No newline at end of file
diff --git a/datasets/linemod/dataset.py b/datasets/linemod/dataset.py
index 16d3e38..a5aacdc 100755
--- a/datasets/linemod/dataset.py
+++ b/datasets/linemod/dataset.py
@@ -157,8 +157,8 @@ class PoseDataset(data.Dataset):
cloud = np.add(cloud, add_t)
#fw = open('evaluation_result/{0}_cld.xyz'.format(index), 'w')
- #for target in cloud:
- # fw.write('{0} {1} {2}\n'.format(target[0], target[1], target[2]))
+ #for it in cloud:
+ # fw.write('{0} {1} {2}\n'.format(it[0], it[1], it[2]))
#fw.close()
model_points = self.pt[obj] / 1000.0
@@ -167,8 +167,8 @@ class PoseDataset(data.Dataset):
model_points = np.delete(model_points, dellist, axis=0)
#fw = open('evaluation_result/{0}_model_points.xyz'.format(index), 'w')
- #for tt in model_points:
- # fw.write('{0} {1} {2}\n'.format(tt[0], tt[1], tt[2]))
+ #for it in model_points:
+ # fw.write('{0} {1} {2}\n'.format(it[0], it[1], it[2]))
#fw.close()
target = np.dot(model_points, target_r.T)
@@ -180,8 +180,8 @@ class PoseDataset(data.Dataset):
out_t = target_t / 1000.0
#fw = open('evaluation_result/{0}_tar.xyz'.format(index), 'w')
- #for tt in target:
- # fw.write('{0} {1} {2}\n'.format(tt[0], tt[1], tt[2]))
+ #for it in target:
+ # fw.write('{0} {1} {2}\n'.format(it[0], it[1], it[2]))
#fw.close()
return torch.from_numpy(cloud.astype(np.float32)), \
diff --git a/datasets/ycb/dataset.py b/datasets/ycb/dataset.py
index 2670a2b..183cd31 100755
--- a/datasets/ycb/dataset.py
+++ b/datasets/ycb/dataset.py
@@ -197,8 +197,8 @@ class PoseDataset(data.Dataset):
cloud = np.add(cloud, add_t)
# fw = open('temp/{0}_cld.xyz'.format(index), 'w')
- # for target in cloud:
- # fw.write('{0} {1} {2}\n'.format(target[0], target[1], target[2]))
+ # for it in cloud:
+ # fw.write('{0} {1} {2}\n'.format(it[0], it[1], it[2]))
# fw.close()
dellist = [j for j in range(0, len(self.cld[obj[idx]]))]
@@ -209,8 +209,8 @@ class PoseDataset(data.Dataset):
model_points = np.delete(self.cld[obj[idx]], dellist, axis=0)
# fw = open('temp/{0}_model_points.xyz'.format(index), 'w')
- # for target in model_points:
- # fw.write('{0} {1} {2}\n'.format(target[0], target[1], target[2]))
+ # for it in model_points:
+ # fw.write('{0} {1} {2}\n'.format(it[0], it[1], it[2]))
# fw.close()
target = np.dot(model_points, target_r.T)
@@ -220,8 +220,8 @@ class PoseDataset(data.Dataset):
target = np.add(target, target_t)
# fw = open('temp/{0}_tar.xyz'.format(index), 'w')
- # for tt in target:
- # fw.write('{0} {1} {2}\n'.format(tt[0], tt[1], tt[2]))
+ # for it in target:
+ # fw.write('{0} {1} {2}\n'.format(it[0], it[1], it[2]))
# fw.close()
return torch.from_numpy(cloud.astype(np.float32)), \
--
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