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API for SuctionNet-1Billion

API for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"

Dataset

Download data and labels from our SuctionNet webpage.

Suction Definition

A suction is defined by its 3D suction point and direction. The direction is a normalized vector pointing outwards the object surface. See the image below.

To evaluate your algorithm, you should represent your predicted suction as a 7-dimensional vector. The first element is your predicted score, the following three elements are 3D suction point coordinate and the last three are normalized direction. For each view (totally 256 views in each scene), say you predict N suctions. The result you save for that view should be a Nx7 numpy array.

Installation

Please install Point Cloud Utils first, then use the following commands

git clone https://github.com/graspnet/suctionnetAPI
cd suctionnetAPI
pip install .

Evaluation Prerequisite

To evaluate predictions, please make sure you pass the completeness check. Refer examples/check_and_explore_data.py to check the completeness.

Examples

We provide several examples to use our API in folder examples

Check, explore and load data: examples/check_and_explore_data.py

Evaluate your results: examples/evaluation.py

Visualize data and labels: visualization.py

Create dense point clouds: dense_pcd.py

Citation

If you find our work useful, please cite

@ARTICLE{suctionnet,
author={Cao, Hanwen and Fang, Hao-Shu and Liu, Wenhai and Lu, Cewu},
journal={IEEE Robotics and Automation Letters}, title={SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping}, year={2021},
volume={6},
number={4},
pages={8718-8725},
doi={10.1109/LRA.2021.3115406}}

About

API tools for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"

graspnet.net/suction

Topics

3d-visiondatasetsuction-grasping

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13 stars

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2 watching

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9 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Repository files navigation
API for SuctionNet-1Billion
API for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
Dataset
Download data and labels from our SuctionNet webpage.
Suction Definition
A suction is defined by its 3D suction point and direction. The direction is a normalized vector pointing outwards the object surface. See the image below.
To evaluate your algorithm, you should represent your predicted suction as a 7-dimensional vector. The first element is your predicted score, the following three elements are 3D suction point coordinate and the last three are normalized direction. For each view (totally 256 views in each scene), say you predict N suctions. The result you save for that view should be a Nx7 numpy array.
Installation
Please install Point Cloud Utils first, then use the following commands
git clone https://github.com/graspnet/suctionnetAPI
cd suctionnetAPI
pip install .

Evaluation Prerequisite
To evaluate predictions, please make sure you pass the completeness check. Refer examples/check_and_explore_data.py to check the completeness.
Examples
We provide several examples to use our API in folder examples
Check, explore and load data: examples/check_and_explore_data.py
Evaluate your results: examples/evaluation.py
Visualize data and labels: visualization.py
Create dense point clouds: dense_pcd.py
Citation
If you find our work useful, please cite
@ARTICLE{suctionnet,
author={Cao, Hanwen and Fang, Hao-Shu and Liu, Wenhai and Lu, Cewu},
journal={IEEE Robotics and Automation Letters}, title={SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping}, year={2021},
volume={6},
number={4},
pages={8718-8725},
doi={10.1109/LRA.2021.3115406}}

About
API tools for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
graspnet.net/suction
Topics
3d-visiondatasetsuction-grasping
Resources
Readme
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
9 forks
Report repository
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, 'i');
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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");
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Repository files navigation
API for SuctionNet-1Billion
API for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
Dataset
Download data and labels from our SuctionNet webpage.
Suction Definition
A suction is defined by its 3D suction point and direction. The direction is a normalized vector pointing outwards the object surface. See the image below.
To evaluate your algorithm, you should represent your predicted suction as a 7-dimensional vector. The first element is your predicted score, the following three elements are 3D suction point coordinate and the last three are normalized direction. For each view (totally 256 views in each scene), say you predict N suctions. The result you save for that view should be a Nx7 numpy array.
Installation
Please install Point Cloud Utils first, then use the following commands
git clone https://github.com/graspnet/suctionnetAPI
cd suctionnetAPI
pip install .

Evaluation Prerequisite
To evaluate predictions, please make sure you pass the completeness check. Refer examples/check_and_explore_data.py to check the completeness.
Examples
We provide several examples to use our API in folder examples
Check, explore and load data: examples/check_and_explore_data.py
Evaluate your results: examples/evaluation.py
Visualize data and labels: visualization.py
Create dense point clouds: dense_pcd.py
Citation
If you find our work useful, please cite
@ARTICLE{suctionnet,
author={Cao, Hanwen and Fang, Hao-Shu and Liu, Wenhai and Lu, Cewu},
journal={IEEE Robotics and Automation Letters}, title={SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping}, year={2021},
volume={6},
number={4},
pages={8718-8725},
doi={10.1109/LRA.2021.3115406}}

About
API tools for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
graspnet.net/suction
Topics
3d-visiondatasetsuction-grasping
Resources
Readme
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
9 forks
Report repository
Releases
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, '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); }
})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
API for SuctionNet-1Billion
API for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
Dataset
Download data and labels from our SuctionNet webpage.
Suction Definition
A suction is defined by its 3D suction point and direction. The direction is a normalized vector pointing outwards the object surface. See the image below.
To evaluate your algorithm, you should represent your predicted suction as a 7-dimensional vector. The first element is your predicted score, the following three elements are 3D suction point coordinate and the last three are normalized direction. For each view (totally 256 views in each scene), say you predict N suctions. The result you save for that view should be a Nx7 numpy array.
Installation
Please install Point Cloud Utils first, then use the following commands
git clone https://github.com/graspnet/suctionnetAPI
cd suctionnetAPI
pip install .

Evaluation Prerequisite
To evaluate predictions, please make sure you pass the completeness check. Refer examples/check_and_explore_data.py to check the completeness.
Examples
We provide several examples to use our API in folder examples
Check, explore and load data: examples/check_and_explore_data.py
Evaluate your results: examples/evaluation.py
Visualize data and labels: visualization.py
Create dense point clouds: dense_pcd.py
Citation
If you find our work useful, please cite
@ARTICLE{suctionnet,
author={Cao, Hanwen and Fang, Hao-Shu and Liu, Wenhai and Lu, Cewu},
journal={IEEE Robotics and Automation Letters}, title={SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping}, year={2021},
volume={6},
number={4},
pages={8718-8725},
doi={10.1109/LRA.2021.3115406}}

About
API tools for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
graspnet.net/suction
Topics
3d-visiondatasetsuction-grasping
Resources
Readme
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
9 forks
Report repository
Releases
Packages
Used by
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); }
})();
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
API for SuctionNet-1Billion
API for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
Dataset
Download data and labels from our SuctionNet webpage.
Suction Definition
A suction is defined by its 3D suction point and direction. The direction is a normalized vector pointing outwards the object surface. See the image below.
To evaluate your algorithm, you should represent your predicted suction as a 7-dimensional vector. The first element is your predicted score, the following three elements are 3D suction point coordinate and the last three are normalized direction. For each view (totally 256 views in each scene), say you predict N suctions. The result you save for that view should be a Nx7 numpy array.
Installation
Please install Point Cloud Utils first, then use the following commands
git clone https://github.com/graspnet/suctionnetAPI
cd suctionnetAPI
pip install .

Evaluation Prerequisite
To evaluate predictions, please make sure you pass the completeness check. Refer examples/check_and_explore_data.py to check the completeness.
Examples
We provide several examples to use our API in folder examples
Check, explore and load data: examples/check_and_explore_data.py
Evaluate your results: examples/evaluation.py
Visualize data and labels: visualization.py
Create dense point clouds: dense_pcd.py
Citation
If you find our work useful, please cite
@ARTICLE{suctionnet,
author={Cao, Hanwen and Fang, Hao-Shu and Liu, Wenhai and Lu, Cewu},
journal={IEEE Robotics and Automation Letters}, title={SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping}, year={2021},
volume={6},
number={4},
pages={8718-8725},
doi={10.1109/LRA.2021.3115406}}

About
API tools for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
graspnet.net/suction
Topics
3d-visiondatasetsuction-grasping
Resources
Readme
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
9 forks
Report repository
Releases
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Used by
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); }
})();
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Repository files navigation
API for SuctionNet-1Billion
API for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
Dataset
Download data and labels from our SuctionNet webpage.
Suction Definition
A suction is defined by its 3D suction point and direction. The direction is a normalized vector pointing outwards the object surface. See the image below.
To evaluate your algorithm, you should represent your predicted suction as a 7-dimensional vector. The first element is your predicted score, the following three elements are 3D suction point coordinate and the last three are normalized direction. For each view (totally 256 views in each scene), say you predict N suctions. The result you save for that view should be a Nx7 numpy array.
Installation
Please install Point Cloud Utils first, then use the following commands
git clone https://github.com/graspnet/suctionnetAPI
cd suctionnetAPI
pip install .

Evaluation Prerequisite
To evaluate predictions, please make sure you pass the completeness check. Refer examples/check_and_explore_data.py to check the completeness.
Examples
We provide several examples to use our API in folder examples
Check, explore and load data: examples/check_and_explore_data.py
Evaluate your results: examples/evaluation.py
Visualize data and labels: visualization.py
Create dense point clouds: dense_pcd.py
Citation
If you find our work useful, please cite
@ARTICLE{suctionnet,
author={Cao, Hanwen and Fang, Hao-Shu and Liu, Wenhai and Lu, Cewu},
journal={IEEE Robotics and Automation Letters}, title={SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping}, year={2021},
volume={6},
number={4},
pages={8718-8725},
doi={10.1109/LRA.2021.3115406}}

About
API tools for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
graspnet.net/suction
Topics
3d-visiondatasetsuction-grasping
Resources
Readme
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
9 forks
Report repository
Releases
Packages
Used by
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); }
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Repository files navigation
API for SuctionNet-1Billion
API for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
Dataset
Download data and labels from our SuctionNet webpage.
Suction Definition
A suction is defined by its 3D suction point and direction. The direction is a normalized vector pointing outwards the object surface. See the image below.
To evaluate your algorithm, you should represent your predicted suction as a 7-dimensional vector. The first element is your predicted score, the following three elements are 3D suction point coordinate and the last three are normalized direction. For each view (totally 256 views in each scene), say you predict N suctions. The result you save for that view should be a Nx7 numpy array.
Installation
Please install Point Cloud Utils first, then use the following commands
git clone https://github.com/graspnet/suctionnetAPI
cd suctionnetAPI
pip install .

Evaluation Prerequisite
To evaluate predictions, please make sure you pass the completeness check. Refer examples/check_and_explore_data.py to check the completeness.
Examples
We provide several examples to use our API in folder examples
Check, explore and load data: examples/check_and_explore_data.py
Evaluate your results: examples/evaluation.py
Visualize data and labels: visualization.py
Create dense point clouds: dense_pcd.py
Citation
If you find our work useful, please cite
@ARTICLE{suctionnet,
author={Cao, Hanwen and Fang, Hao-Shu and Liu, Wenhai and Lu, Cewu},
journal={IEEE Robotics and Automation Letters}, title={SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping}, year={2021},
volume={6},
number={4},
pages={8718-8725},
doi={10.1109/LRA.2021.3115406}}

About
API tools for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
graspnet.net/suction
Topics
3d-visiondatasetsuction-grasping
Resources
Readme
Activity
Custom properties
Stars
13 stars
Watchers
2 watching
Forks
9 forks
Report repository
Releases
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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API for SuctionNet-1Billion
API for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
Dataset
Download data and labels from our SuctionNet webpage.
Suction Definition
A suction is defined by its 3D suction point and direction. The direction is a normalized vector pointing outwards the object surface. See the image below.
To evaluate your algorithm, you should represent your predicted suction as a 7-dimensional vector. The first element is your predicted score, the following three elements are 3D suction point coordinate and the last three are normalized direction. For each view (totally 256 views in each scene), say you predict N suctions. The result you save for that view should be a Nx7 numpy array.
Installation
Please install Point Cloud Utils first, then use the following commands
git clone https://github.com/graspnet/suctionnetAPI
cd suctionnetAPI
pip install .

Evaluation Prerequisite
To evaluate predictions, please make sure you pass the completeness check. Refer examples/check_and_explore_data.py to check the completeness.
Examples
We provide several examples to use our API in folder examples
Check, explore and load data: examples/check_and_explore_data.py
Evaluate your results: examples/evaluation.py
Visualize data and labels: visualization.py
Create dense point clouds: dense_pcd.py
Citation
If you find our work useful, please cite
@ARTICLE{suctionnet,
author={Cao, Hanwen and Fang, Hao-Shu and Liu, Wenhai and Lu, Cewu},
journal={IEEE Robotics and Automation Letters}, title={SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping}, year={2021},
volume={6},
number={4},
pages={8718-8725},
doi={10.1109/LRA.2021.3115406}}

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API tools for SuctionNet-1Billion dataset of RA-L paper "SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping"
graspnet.net/suction
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