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grasp_multiObject

Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data.
This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.

drawing

Usage

Download RGB-D data and put into grasp_multiObject/rgbd/

Each testing data has one RGB image (rgb_xxxx) and one depth image (depth_xxxx).
The corresponding grasp annotation (rgb_xxxx_annotations) can be found in grasp_multiObject/annotation/

Generate RG-D data?

mkdir rgd
run rgbd2rgd

you will have RG-D data in grasp_multiObject/rgd/

Crop images?

mkdir rgd_cropped320
mkdir rgb_cropped320
run image2txt

you will have cropped RGB and RGD images in grasp_multiObject/rgd_cropped320/ and grasp_multiObject/rgb_cropped320/, respectively.

also, you will have corresponding annotation files, as well as a full list of image path.

Visualize grasp?

run visualizationGripper

this file shows a simple example to visualize ground truth grasps

Annotate your own data?

git clone https://github.com/ivalab/grasp_annotation_tool

you can annotate grasps on your own data with this simple tool!
Both dataset and annotation tool can also be found here

Citation

If you find it helpful for your research, please consider citing:

@inproceedings{chu2018deep,
title = {Real-World Multiobject, Multigrasp Detection},
author = {F. Chu and R. Xu and P. A. Vela},
journal = {IEEE Robotics and Automation Letters},
year = {2018},
volume = {3},
number = {4},
pages = {3355-3362},
DOI = {10.1109/LRA.2018.2852777},
ISSN = {2377-3766},
month = {Oct}
}

If you encounter any questions, please contact me at fujenchu[at]gatech[dot]edu

About

Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data. This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.

Topics

cornell-datasetdatasetgraspgraspingiros2018ra-lrobotics

Resources

Readme
Activity
Custom properties

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114 stars

Watchers

3 watching

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20 forks
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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grasp_multiObject
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data.

This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
drawing
Usage
Download RGB-D data and put into grasp_multiObject/rgbd/
Each testing data has one RGB image (rgb_xxxx) and one depth image (depth_xxxx).

The corresponding grasp annotation (rgb_xxxx_annotations) can be found in grasp_multiObject/annotation/
Generate RG-D data?
mkdir rgd
run rgbd2rgd

you will have RG-D data in grasp_multiObject/rgd/
Crop images?
mkdir rgd_cropped320
mkdir rgb_cropped320
run image2txt

you will have cropped RGB and RGD images in grasp_multiObject/rgd_cropped320/ and grasp_multiObject/rgb_cropped320/, respectively.
also, you will have corresponding annotation files, as well as a full list of image path.
Visualize grasp?
run visualizationGripper

this file shows a simple example to visualize ground truth grasps
Annotate your own data?
git clone https://github.com/ivalab/grasp_annotation_tool

you can annotate grasps on your own data with this simple tool!

Both dataset and annotation tool can also be found here
Citation
If you find it helpful for your research, please consider citing:
@inproceedings{chu2018deep,
title = {Real-World Multiobject, Multigrasp Detection},
author = {F. Chu and R. Xu and P. A. Vela},
journal = {IEEE Robotics and Automation Letters},
year = {2018},
volume = {3},
number = {4},
pages = {3355-3362},
DOI = {10.1109/LRA.2018.2852777},
ISSN = {2377-3766},
month = {Oct}
}

If you encounter any questions, please contact me at fujenchu[at]gatech[dot]edu
About
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data. This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
Topics
cornell-datasetdatasetgraspgraspingiros2018ra-lrobotics
Resources
Readme
Activity
Custom properties
Stars
114 stars
Watchers
3 watching
Forks
20 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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grasp_multiObject
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data.

This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
drawing
Usage
Download RGB-D data and put into grasp_multiObject/rgbd/
Each testing data has one RGB image (rgb_xxxx) and one depth image (depth_xxxx).

The corresponding grasp annotation (rgb_xxxx_annotations) can be found in grasp_multiObject/annotation/
Generate RG-D data?
mkdir rgd
run rgbd2rgd

you will have RG-D data in grasp_multiObject/rgd/
Crop images?
mkdir rgd_cropped320
mkdir rgb_cropped320
run image2txt

you will have cropped RGB and RGD images in grasp_multiObject/rgd_cropped320/ and grasp_multiObject/rgb_cropped320/, respectively.
also, you will have corresponding annotation files, as well as a full list of image path.
Visualize grasp?
run visualizationGripper

this file shows a simple example to visualize ground truth grasps
Annotate your own data?
git clone https://github.com/ivalab/grasp_annotation_tool

you can annotate grasps on your own data with this simple tool!

Both dataset and annotation tool can also be found here
Citation
If you find it helpful for your research, please consider citing:
@inproceedings{chu2018deep,
title = {Real-World Multiobject, Multigrasp Detection},
author = {F. Chu and R. Xu and P. A. Vela},
journal = {IEEE Robotics and Automation Letters},
year = {2018},
volume = {3},
number = {4},
pages = {3355-3362},
DOI = {10.1109/LRA.2018.2852777},
ISSN = {2377-3766},
month = {Oct}
}

If you encounter any questions, please contact me at fujenchu[at]gatech[dot]edu
About
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data. This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
Topics
cornell-datasetdatasetgraspgraspingiros2018ra-lrobotics
Resources
Readme
Activity
Custom properties
Stars
114 stars
Watchers
3 watching
Forks
20 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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grasp_multiObject
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data.

This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
drawing
Usage
Download RGB-D data and put into grasp_multiObject/rgbd/
Each testing data has one RGB image (rgb_xxxx) and one depth image (depth_xxxx).

The corresponding grasp annotation (rgb_xxxx_annotations) can be found in grasp_multiObject/annotation/
Generate RG-D data?
mkdir rgd
run rgbd2rgd

you will have RG-D data in grasp_multiObject/rgd/
Crop images?
mkdir rgd_cropped320
mkdir rgb_cropped320
run image2txt

you will have cropped RGB and RGD images in grasp_multiObject/rgd_cropped320/ and grasp_multiObject/rgb_cropped320/, respectively.
also, you will have corresponding annotation files, as well as a full list of image path.
Visualize grasp?
run visualizationGripper

this file shows a simple example to visualize ground truth grasps
Annotate your own data?
git clone https://github.com/ivalab/grasp_annotation_tool

you can annotate grasps on your own data with this simple tool!

Both dataset and annotation tool can also be found here
Citation
If you find it helpful for your research, please consider citing:
@inproceedings{chu2018deep,
title = {Real-World Multiobject, Multigrasp Detection},
author = {F. Chu and R. Xu and P. A. Vela},
journal = {IEEE Robotics and Automation Letters},
year = {2018},
volume = {3},
number = {4},
pages = {3355-3362},
DOI = {10.1109/LRA.2018.2852777},
ISSN = {2377-3766},
month = {Oct}
}

If you encounter any questions, please contact me at fujenchu[at]gatech[dot]edu
About
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data. This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
Topics
cornell-datasetdatasetgraspgraspingiros2018ra-lrobotics
Resources
Readme
Activity
Custom properties
Stars
114 stars
Watchers
3 watching
Forks
20 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
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grasp_multiObject
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data.

This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
drawing
Usage
Download RGB-D data and put into grasp_multiObject/rgbd/
Each testing data has one RGB image (rgb_xxxx) and one depth image (depth_xxxx).

The corresponding grasp annotation (rgb_xxxx_annotations) can be found in grasp_multiObject/annotation/
Generate RG-D data?
mkdir rgd
run rgbd2rgd

you will have RG-D data in grasp_multiObject/rgd/
Crop images?
mkdir rgd_cropped320
mkdir rgb_cropped320
run image2txt

you will have cropped RGB and RGD images in grasp_multiObject/rgd_cropped320/ and grasp_multiObject/rgb_cropped320/, respectively.
also, you will have corresponding annotation files, as well as a full list of image path.
Visualize grasp?
run visualizationGripper

this file shows a simple example to visualize ground truth grasps
Annotate your own data?
git clone https://github.com/ivalab/grasp_annotation_tool

you can annotate grasps on your own data with this simple tool!

Both dataset and annotation tool can also be found here
Citation
If you find it helpful for your research, please consider citing:
@inproceedings{chu2018deep,
title = {Real-World Multiobject, Multigrasp Detection},
author = {F. Chu and R. Xu and P. A. Vela},
journal = {IEEE Robotics and Automation Letters},
year = {2018},
volume = {3},
number = {4},
pages = {3355-3362},
DOI = {10.1109/LRA.2018.2852777},
ISSN = {2377-3766},
month = {Oct}
}

If you encounter any questions, please contact me at fujenchu[at]gatech[dot]edu
About
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data. This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
Topics
cornell-datasetdatasetgraspgraspingiros2018ra-lrobotics
Resources
Readme
Activity
Custom properties
Stars
114 stars
Watchers
3 watching
Forks
20 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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grasp_multiObject
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data.

This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
drawing
Usage
Download RGB-D data and put into grasp_multiObject/rgbd/
Each testing data has one RGB image (rgb_xxxx) and one depth image (depth_xxxx).

The corresponding grasp annotation (rgb_xxxx_annotations) can be found in grasp_multiObject/annotation/
Generate RG-D data?
mkdir rgd
run rgbd2rgd

you will have RG-D data in grasp_multiObject/rgd/
Crop images?
mkdir rgd_cropped320
mkdir rgb_cropped320
run image2txt

you will have cropped RGB and RGD images in grasp_multiObject/rgd_cropped320/ and grasp_multiObject/rgb_cropped320/, respectively.
also, you will have corresponding annotation files, as well as a full list of image path.
Visualize grasp?
run visualizationGripper

this file shows a simple example to visualize ground truth grasps
Annotate your own data?
git clone https://github.com/ivalab/grasp_annotation_tool

you can annotate grasps on your own data with this simple tool!

Both dataset and annotation tool can also be found here
Citation
If you find it helpful for your research, please consider citing:
@inproceedings{chu2018deep,
title = {Real-World Multiobject, Multigrasp Detection},
author = {F. Chu and R. Xu and P. A. Vela},
journal = {IEEE Robotics and Automation Letters},
year = {2018},
volume = {3},
number = {4},
pages = {3355-3362},
DOI = {10.1109/LRA.2018.2852777},
ISSN = {2377-3766},
month = {Oct}
}

If you encounter any questions, please contact me at fujenchu[at]gatech[dot]edu
About
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data. This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
Topics
cornell-datasetdatasetgraspgraspingiros2018ra-lrobotics
Resources
Readme
Activity
Custom properties
Stars
114 stars
Watchers
3 watching
Forks
20 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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grasp_multiObject
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data.

This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
drawing
Usage
Download RGB-D data and put into grasp_multiObject/rgbd/
Each testing data has one RGB image (rgb_xxxx) and one depth image (depth_xxxx).

The corresponding grasp annotation (rgb_xxxx_annotations) can be found in grasp_multiObject/annotation/
Generate RG-D data?
mkdir rgd
run rgbd2rgd

you will have RG-D data in grasp_multiObject/rgd/
Crop images?
mkdir rgd_cropped320
mkdir rgb_cropped320
run image2txt

you will have cropped RGB and RGD images in grasp_multiObject/rgd_cropped320/ and grasp_multiObject/rgb_cropped320/, respectively.
also, you will have corresponding annotation files, as well as a full list of image path.
Visualize grasp?
run visualizationGripper

this file shows a simple example to visualize ground truth grasps
Annotate your own data?
git clone https://github.com/ivalab/grasp_annotation_tool

you can annotate grasps on your own data with this simple tool!

Both dataset and annotation tool can also be found here
Citation
If you find it helpful for your research, please consider citing:
@inproceedings{chu2018deep,
title = {Real-World Multiobject, Multigrasp Detection},
author = {F. Chu and R. Xu and P. A. Vela},
journal = {IEEE Robotics and Automation Letters},
year = {2018},
volume = {3},
number = {4},
pages = {3355-3362},
DOI = {10.1109/LRA.2018.2852777},
ISSN = {2377-3766},
month = {Oct}
}

If you encounter any questions, please contact me at fujenchu[at]gatech[dot]edu
About
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data. This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
Topics
cornell-datasetdatasetgraspgraspingiros2018ra-lrobotics
Resources
Readme
Activity
Custom properties
Stars
114 stars
Watchers
3 watching
Forks
20 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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grasp_multiObject
Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data.

This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
drawing
Usage
Download RGB-D data and put into grasp_multiObject/rgbd/
Each testing data has one RGB image (rgb_xxxx) and one depth image (depth_xxxx).

The corresponding grasp annotation (rgb_xxxx_annotations) can be found in grasp_multiObject/annotation/
Generate RG-D data?
mkdir rgd
run rgbd2rgd

you will have RG-D data in grasp_multiObject/rgd/
Crop images?
mkdir rgd_cropped320
mkdir rgb_cropped320
run image2txt

you will have cropped RGB and RGD images in grasp_multiObject/rgd_cropped320/ and grasp_multiObject/rgb_cropped320/, respectively.
also, you will have corresponding annotation files, as well as a full list of image path.
Visualize grasp?
run visualizationGripper

this file shows a simple example to visualize ground truth grasps
Annotate your own data?
git clone https://github.com/ivalab/grasp_annotation_tool

you can annotate grasps on your own data with this simple tool!

Both dataset and annotation tool can also be found here
Citation
If you find it helpful for your research, please consider citing:
@inproceedings{chu2018deep,
title = {Real-World Multiobject, Multigrasp Detection},
author = {F. Chu and R. Xu and P. A. Vela},
journal = {IEEE Robotics and Automation Letters},
year = {2018},
volume = {3},
number = {4},
pages = {3355-3362},
DOI = {10.1109/LRA.2018.2852777},
ISSN = {2377-3766},
month = {Oct}
}

If you encounter any questions, please contact me at fujenchu[at]gatech[dot]edu
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Robotic grasp dataset for multi-object multi-grasp evaluation with RGB-D data. This dataset is annotated using the same protocal as Cornell Dataset, and can be used as multi-object extension of Cornell Dataset.
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