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completion3D: Stanford 3D Object Point Cloud Completion Benchmark &

TopNet: Structural Point Cloud Decoder

This repository contains source code for all methods used for the Stanford 3D Object Point Cloud Completion Benchmark and presented in the paper TopNet: Structural Point Cloud Decoder, CVPR 2019.

Project Pages

The TopNet project page is available at https://completion3D.stanford.edu/topnet. The completion3D benchmark is available at http://completion3D.stanford.edu.

Overview

The completion3D benchmark is a platform for evaluating state-of-the-art 3D Object Point Cloud Completion methods. This repository contains source code for various methods evaluated on the benchmark. Both Tensorflow and Pytorch are supported. Overview3D Object Point Cloud Completion Results: A partial 3D point cloud is given as input and various methods used to generate a completed 3D point cloud

Benchmark submission instructions

To submit to the completion3d benchmark, set TRAIN=0 and BENCHMARK=1 in run.sh and run the script with parameters to evaluate. A submission.zip file will be generated by the script in the experiment output folder.

Citing this work

If you find this work useful in your research, please consider citing:

@inproceedings{topnet2019,
title={TopNet: Structural Point Cloud Decoder},
author={Tchapmi, Lyne P and Kosaraju, Vineet and Rezatofighi, S. Hamid and Reid, Ian and Savarese, Silvio},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
@inProceedings{yuan2018pcn,
title = {PCN: Point Completion Network},
author = {Yuan, Wentao and Khot, Tejas and Held, David and Mertz, Christoph and Hebert, Martial},
booktitle = {3D Vision (3DV), 2018 International Conference on},
year = {2018}
}
@article{DBLP:journals/corr/ChangFGHHLSSSSX15,
author = {Angel X. Chang and Thomas A. Funkhouser and Leonidas J. Guibas and Pat Hanrahan and Qi{-}Xing Huang and Zimo Li and Silvio Savarese and Manolis Savva and Shuran Song and Hao Su and Jianxiong Xiao and Li Yi and Fisher Yu},
title = {ShapeNet: An Information-Rich 3D Model Repository},
journal = {CoRR},
volume = {abs/1512.03012},
year = {2015},
url = {http://arxiv.org/abs/1512.03012},
archivePrefix = {arXiv},
eprint = {1512.03012},
timestamp = {Mon, 13 Aug 2018 16:47:39 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/ChangFGHHLSSSSX15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

And please refer to the Shapenet Terms of Use

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About

Source code for baselines of the Stanford 3D Point Cloud Completion Benchmark (completion3d.stanford.edu) and TopNet: Structural Point Cloud Decoder, CVPR 2019

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240 stars

Watchers

9 watching

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

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BranchesTags
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completion3D: Stanford 3D Object Point Cloud Completion Benchmark &
TopNet: Structural Point Cloud Decoder
This repository contains source code for all methods used for the Stanford 3D Object Point Cloud Completion Benchmark and presented in the paper TopNet: Structural Point Cloud Decoder, CVPR 2019.
Project Pages
The TopNet project page is available at https://completion3D.stanford.edu/topnet.
The completion3D benchmark is available at http://completion3D.stanford.edu.
Overview
The completion3D benchmark is a platform for evaluating state-of-the-art 3D Object Point Cloud Completion methods. This repository contains source code for various methods evaluated on the benchmark. Both Tensorflow and Pytorch are supported.
Overview3D Object Point Cloud Completion Results: A partial 3D point cloud is given as input and various methods used to generate a completed 3D point cloud
Benchmark submission instructions
To submit to the completion3d benchmark, set TRAIN=0 and BENCHMARK=1 in run.sh and run the script with parameters to evaluate. A submission.zip file will be generated by the script in the experiment output folder.
Citing this work
If you find this work useful in your research, please consider citing:
@inproceedings{topnet2019,
title={TopNet: Structural Point Cloud Decoder},
author={Tchapmi, Lyne P and Kosaraju, Vineet and Rezatofighi, S. Hamid and Reid, Ian and Savarese, Silvio},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
@inProceedings{yuan2018pcn,
title = {PCN: Point Completion Network},
author = {Yuan, Wentao and Khot, Tejas and Held, David and Mertz, Christoph and Hebert, Martial},
booktitle = {3D Vision (3DV), 2018 International Conference on},
year = {2018}
}
@article{DBLP:journals/corr/ChangFGHHLSSSSX15,
author = {Angel X. Chang and Thomas A. Funkhouser and Leonidas J. Guibas and Pat Hanrahan and Qi{-}Xing Huang and Zimo Li and Silvio Savarese and Manolis Savva and Shuran Song and Hao Su and Jianxiong Xiao and Li Yi and Fisher Yu},
title = {ShapeNet: An Information-Rich 3D Model Repository},
journal = {CoRR},
volume = {abs/1512.03012},
year = {2015},
url = {http://arxiv.org/abs/1512.03012},
archivePrefix = {arXiv},
eprint = {1512.03012},
timestamp = {Mon, 13 Aug 2018 16:47:39 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/ChangFGHHLSSSSX15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

And please refer to the Shapenet Terms of Use
License
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About
Source code for baselines of the Stanford 3D Point Cloud Completion Benchmark (completion3d.stanford.edu) and TopNet: Structural Point Cloud Decoder, CVPR 2019
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, '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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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completion3D: Stanford 3D Object Point Cloud Completion Benchmark &
TopNet: Structural Point Cloud Decoder
This repository contains source code for all methods used for the Stanford 3D Object Point Cloud Completion Benchmark and presented in the paper TopNet: Structural Point Cloud Decoder, CVPR 2019.
Project Pages
The TopNet project page is available at https://completion3D.stanford.edu/topnet.
The completion3D benchmark is available at http://completion3D.stanford.edu.
Overview
The completion3D benchmark is a platform for evaluating state-of-the-art 3D Object Point Cloud Completion methods. This repository contains source code for various methods evaluated on the benchmark. Both Tensorflow and Pytorch are supported.
Overview3D Object Point Cloud Completion Results: A partial 3D point cloud is given as input and various methods used to generate a completed 3D point cloud
Benchmark submission instructions
To submit to the completion3d benchmark, set TRAIN=0 and BENCHMARK=1 in run.sh and run the script with parameters to evaluate. A submission.zip file will be generated by the script in the experiment output folder.
Citing this work
If you find this work useful in your research, please consider citing:
@inproceedings{topnet2019,
title={TopNet: Structural Point Cloud Decoder},
author={Tchapmi, Lyne P and Kosaraju, Vineet and Rezatofighi, S. Hamid and Reid, Ian and Savarese, Silvio},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
@inProceedings{yuan2018pcn,
title = {PCN: Point Completion Network},
author = {Yuan, Wentao and Khot, Tejas and Held, David and Mertz, Christoph and Hebert, Martial},
booktitle = {3D Vision (3DV), 2018 International Conference on},
year = {2018}
}
@article{DBLP:journals/corr/ChangFGHHLSSSSX15,
author = {Angel X. Chang and Thomas A. Funkhouser and Leonidas J. Guibas and Pat Hanrahan and Qi{-}Xing Huang and Zimo Li and Silvio Savarese and Manolis Savva and Shuran Song and Hao Su and Jianxiong Xiao and Li Yi and Fisher Yu},
title = {ShapeNet: An Information-Rich 3D Model Repository},
journal = {CoRR},
volume = {abs/1512.03012},
year = {2015},
url = {http://arxiv.org/abs/1512.03012},
archivePrefix = {arXiv},
eprint = {1512.03012},
timestamp = {Mon, 13 Aug 2018 16:47:39 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/ChangFGHHLSSSSX15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

And please refer to the Shapenet Terms of Use
License
MIT License
About
Source code for baselines of the Stanford 3D Point Cloud Completion Benchmark (completion3d.stanford.edu) and TopNet: Structural Point Cloud Decoder, CVPR 2019
Resources
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Stars
240 stars
Watchers
9 watching
Forks
37 forks
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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); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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completion3D: Stanford 3D Object Point Cloud Completion Benchmark &
TopNet: Structural Point Cloud Decoder
This repository contains source code for all methods used for the Stanford 3D Object Point Cloud Completion Benchmark and presented in the paper TopNet: Structural Point Cloud Decoder, CVPR 2019.
Project Pages
The TopNet project page is available at https://completion3D.stanford.edu/topnet.
The completion3D benchmark is available at http://completion3D.stanford.edu.
Overview
The completion3D benchmark is a platform for evaluating state-of-the-art 3D Object Point Cloud Completion methods. This repository contains source code for various methods evaluated on the benchmark. Both Tensorflow and Pytorch are supported.
Overview3D Object Point Cloud Completion Results: A partial 3D point cloud is given as input and various methods used to generate a completed 3D point cloud
Benchmark submission instructions
To submit to the completion3d benchmark, set TRAIN=0 and BENCHMARK=1 in run.sh and run the script with parameters to evaluate. A submission.zip file will be generated by the script in the experiment output folder.
Citing this work
If you find this work useful in your research, please consider citing:
@inproceedings{topnet2019,
title={TopNet: Structural Point Cloud Decoder},
author={Tchapmi, Lyne P and Kosaraju, Vineet and Rezatofighi, S. Hamid and Reid, Ian and Savarese, Silvio},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
@inProceedings{yuan2018pcn,
title = {PCN: Point Completion Network},
author = {Yuan, Wentao and Khot, Tejas and Held, David and Mertz, Christoph and Hebert, Martial},
booktitle = {3D Vision (3DV), 2018 International Conference on},
year = {2018}
}
@article{DBLP:journals/corr/ChangFGHHLSSSSX15,
author = {Angel X. Chang and Thomas A. Funkhouser and Leonidas J. Guibas and Pat Hanrahan and Qi{-}Xing Huang and Zimo Li and Silvio Savarese and Manolis Savva and Shuran Song and Hao Su and Jianxiong Xiao and Li Yi and Fisher Yu},
title = {ShapeNet: An Information-Rich 3D Model Repository},
journal = {CoRR},
volume = {abs/1512.03012},
year = {2015},
url = {http://arxiv.org/abs/1512.03012},
archivePrefix = {arXiv},
eprint = {1512.03012},
timestamp = {Mon, 13 Aug 2018 16:47:39 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/ChangFGHHLSSSSX15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

And please refer to the Shapenet Terms of Use
License
MIT License
About
Source code for baselines of the Stanford 3D Point Cloud Completion Benchmark (completion3d.stanford.edu) and TopNet: Structural Point Cloud Decoder, CVPR 2019
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, '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" + '

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BranchesTags
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completion3D: Stanford 3D Object Point Cloud Completion Benchmark &
TopNet: Structural Point Cloud Decoder
This repository contains source code for all methods used for the Stanford 3D Object Point Cloud Completion Benchmark and presented in the paper TopNet: Structural Point Cloud Decoder, CVPR 2019.
Project Pages
The TopNet project page is available at https://completion3D.stanford.edu/topnet.
The completion3D benchmark is available at http://completion3D.stanford.edu.
Overview
The completion3D benchmark is a platform for evaluating state-of-the-art 3D Object Point Cloud Completion methods. This repository contains source code for various methods evaluated on the benchmark. Both Tensorflow and Pytorch are supported.
Overview3D Object Point Cloud Completion Results: A partial 3D point cloud is given as input and various methods used to generate a completed 3D point cloud
Benchmark submission instructions
To submit to the completion3d benchmark, set TRAIN=0 and BENCHMARK=1 in run.sh and run the script with parameters to evaluate. A submission.zip file will be generated by the script in the experiment output folder.
Citing this work
If you find this work useful in your research, please consider citing:
@inproceedings{topnet2019,
title={TopNet: Structural Point Cloud Decoder},
author={Tchapmi, Lyne P and Kosaraju, Vineet and Rezatofighi, S. Hamid and Reid, Ian and Savarese, Silvio},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
@inProceedings{yuan2018pcn,
title = {PCN: Point Completion Network},
author = {Yuan, Wentao and Khot, Tejas and Held, David and Mertz, Christoph and Hebert, Martial},
booktitle = {3D Vision (3DV), 2018 International Conference on},
year = {2018}
}
@article{DBLP:journals/corr/ChangFGHHLSSSSX15,
author = {Angel X. Chang and Thomas A. Funkhouser and Leonidas J. Guibas and Pat Hanrahan and Qi{-}Xing Huang and Zimo Li and Silvio Savarese and Manolis Savva and Shuran Song and Hao Su and Jianxiong Xiao and Li Yi and Fisher Yu},
title = {ShapeNet: An Information-Rich 3D Model Repository},
journal = {CoRR},
volume = {abs/1512.03012},
year = {2015},
url = {http://arxiv.org/abs/1512.03012},
archivePrefix = {arXiv},
eprint = {1512.03012},
timestamp = {Mon, 13 Aug 2018 16:47:39 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/ChangFGHHLSSSSX15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

And please refer to the Shapenet Terms of Use
License
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About
Source code for baselines of the Stanford 3D Point Cloud Completion Benchmark (completion3d.stanford.edu) and TopNet: Structural Point Cloud Decoder, CVPR 2019
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, '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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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completion3D: Stanford 3D Object Point Cloud Completion Benchmark &
TopNet: Structural Point Cloud Decoder
This repository contains source code for all methods used for the Stanford 3D Object Point Cloud Completion Benchmark and presented in the paper TopNet: Structural Point Cloud Decoder, CVPR 2019.
Project Pages
The TopNet project page is available at https://completion3D.stanford.edu/topnet.
The completion3D benchmark is available at http://completion3D.stanford.edu.
Overview
The completion3D benchmark is a platform for evaluating state-of-the-art 3D Object Point Cloud Completion methods. This repository contains source code for various methods evaluated on the benchmark. Both Tensorflow and Pytorch are supported.
Overview3D Object Point Cloud Completion Results: A partial 3D point cloud is given as input and various methods used to generate a completed 3D point cloud
Benchmark submission instructions
To submit to the completion3d benchmark, set TRAIN=0 and BENCHMARK=1 in run.sh and run the script with parameters to evaluate. A submission.zip file will be generated by the script in the experiment output folder.
Citing this work
If you find this work useful in your research, please consider citing:
@inproceedings{topnet2019,
title={TopNet: Structural Point Cloud Decoder},
author={Tchapmi, Lyne P and Kosaraju, Vineet and Rezatofighi, S. Hamid and Reid, Ian and Savarese, Silvio},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
@inProceedings{yuan2018pcn,
title = {PCN: Point Completion Network},
author = {Yuan, Wentao and Khot, Tejas and Held, David and Mertz, Christoph and Hebert, Martial},
booktitle = {3D Vision (3DV), 2018 International Conference on},
year = {2018}
}
@article{DBLP:journals/corr/ChangFGHHLSSSSX15,
author = {Angel X. Chang and Thomas A. Funkhouser and Leonidas J. Guibas and Pat Hanrahan and Qi{-}Xing Huang and Zimo Li and Silvio Savarese and Manolis Savva and Shuran Song and Hao Su and Jianxiong Xiao and Li Yi and Fisher Yu},
title = {ShapeNet: An Information-Rich 3D Model Repository},
journal = {CoRR},
volume = {abs/1512.03012},
year = {2015},
url = {http://arxiv.org/abs/1512.03012},
archivePrefix = {arXiv},
eprint = {1512.03012},
timestamp = {Mon, 13 Aug 2018 16:47:39 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/ChangFGHHLSSSSX15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

And please refer to the Shapenet Terms of Use
License
MIT License
About
Source code for baselines of the Stanford 3D Point Cloud Completion Benchmark (completion3d.stanford.edu) and TopNet: Structural Point Cloud Decoder, CVPR 2019
Resources
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Stars
240 stars
Watchers
9 watching
Forks
37 forks
Report repository
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Used by
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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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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completion3D: Stanford 3D Object Point Cloud Completion Benchmark &
TopNet: Structural Point Cloud Decoder
This repository contains source code for all methods used for the Stanford 3D Object Point Cloud Completion Benchmark and presented in the paper TopNet: Structural Point Cloud Decoder, CVPR 2019.
Project Pages
The TopNet project page is available at https://completion3D.stanford.edu/topnet.
The completion3D benchmark is available at http://completion3D.stanford.edu.
Overview
The completion3D benchmark is a platform for evaluating state-of-the-art 3D Object Point Cloud Completion methods. This repository contains source code for various methods evaluated on the benchmark. Both Tensorflow and Pytorch are supported.
Overview3D Object Point Cloud Completion Results: A partial 3D point cloud is given as input and various methods used to generate a completed 3D point cloud
Benchmark submission instructions
To submit to the completion3d benchmark, set TRAIN=0 and BENCHMARK=1 in run.sh and run the script with parameters to evaluate. A submission.zip file will be generated by the script in the experiment output folder.
Citing this work
If you find this work useful in your research, please consider citing:
@inproceedings{topnet2019,
title={TopNet: Structural Point Cloud Decoder},
author={Tchapmi, Lyne P and Kosaraju, Vineet and Rezatofighi, S. Hamid and Reid, Ian and Savarese, Silvio},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
@inProceedings{yuan2018pcn,
title = {PCN: Point Completion Network},
author = {Yuan, Wentao and Khot, Tejas and Held, David and Mertz, Christoph and Hebert, Martial},
booktitle = {3D Vision (3DV), 2018 International Conference on},
year = {2018}
}
@article{DBLP:journals/corr/ChangFGHHLSSSSX15,
author = {Angel X. Chang and Thomas A. Funkhouser and Leonidas J. Guibas and Pat Hanrahan and Qi{-}Xing Huang and Zimo Li and Silvio Savarese and Manolis Savva and Shuran Song and Hao Su and Jianxiong Xiao and Li Yi and Fisher Yu},
title = {ShapeNet: An Information-Rich 3D Model Repository},
journal = {CoRR},
volume = {abs/1512.03012},
year = {2015},
url = {http://arxiv.org/abs/1512.03012},
archivePrefix = {arXiv},
eprint = {1512.03012},
timestamp = {Mon, 13 Aug 2018 16:47:39 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/ChangFGHHLSSSSX15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

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Source code for baselines of the Stanford 3D Point Cloud Completion Benchmark (completion3d.stanford.edu) and TopNet: Structural Point Cloud Decoder, CVPR 2019
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, 'i');
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completion3D: Stanford 3D Object Point Cloud Completion Benchmark &
TopNet: Structural Point Cloud Decoder
This repository contains source code for all methods used for the Stanford 3D Object Point Cloud Completion Benchmark and presented in the paper TopNet: Structural Point Cloud Decoder, CVPR 2019.
Project Pages
The TopNet project page is available at https://completion3D.stanford.edu/topnet.
The completion3D benchmark is available at http://completion3D.stanford.edu.
Overview
The completion3D benchmark is a platform for evaluating state-of-the-art 3D Object Point Cloud Completion methods. This repository contains source code for various methods evaluated on the benchmark. Both Tensorflow and Pytorch are supported.
Overview3D Object Point Cloud Completion Results: A partial 3D point cloud is given as input and various methods used to generate a completed 3D point cloud
Benchmark submission instructions
To submit to the completion3d benchmark, set TRAIN=0 and BENCHMARK=1 in run.sh and run the script with parameters to evaluate. A submission.zip file will be generated by the script in the experiment output folder.
Citing this work
If you find this work useful in your research, please consider citing:
@inproceedings{topnet2019,
title={TopNet: Structural Point Cloud Decoder},
author={Tchapmi, Lyne P and Kosaraju, Vineet and Rezatofighi, S. Hamid and Reid, Ian and Savarese, Silvio},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
@inProceedings{yuan2018pcn,
title = {PCN: Point Completion Network},
author = {Yuan, Wentao and Khot, Tejas and Held, David and Mertz, Christoph and Hebert, Martial},
booktitle = {3D Vision (3DV), 2018 International Conference on},
year = {2018}
}
@article{DBLP:journals/corr/ChangFGHHLSSSSX15,
author = {Angel X. Chang and Thomas A. Funkhouser and Leonidas J. Guibas and Pat Hanrahan and Qi{-}Xing Huang and Zimo Li and Silvio Savarese and Manolis Savva and Shuran Song and Hao Su and Jianxiong Xiao and Li Yi and Fisher Yu},
title = {ShapeNet: An Information-Rich 3D Model Repository},
journal = {CoRR},
volume = {abs/1512.03012},
year = {2015},
url = {http://arxiv.org/abs/1512.03012},
archivePrefix = {arXiv},
eprint = {1512.03012},
timestamp = {Mon, 13 Aug 2018 16:47:39 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/ChangFGHHLSSSSX15},
bibsource = {dblp computer science bibliography, https://dblp.org}
}

And please refer to the Shapenet Terms of Use
License
MIT License
About
Source code for baselines of the Stanford 3D Point Cloud Completion Benchmark (completion3d.stanford.edu) and TopNet: Structural Point Cloud Decoder, CVPR 2019
Resources
Readme
Activity
Stars
240 stars
Watchers
9 watching
Forks
37 forks
Report repository
Releases
Packages
Used by
Contributors
Languages