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Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points

prediction example

Introduction

This work is based on our arXiv tech report.

We propose a deep hierarchical Encoder-Decoder architecture with point atrous convolution to exploit multi-scale edge-aware features in unorganized 3D points.

Experimental results show that our network outperform previous state-of-the-art methods, in 3D object classification, object-part segmentation and semantic segmentation. In particular, our proposed modules are more efficient (in terms of required training time and memory footprint) than previous networks which heavily rely on neighboring points.

We encourage you to apply our proposed modules for more complicated point cloud applications.

Installation

The code has been tested with Tensorflow 1.4, CUDA 8.0 and Tensorflow 1.12, CUDA 9.0

  1. install required python libs
  2. download correspoinding dataset
  3. compile all the tensorflow ops

Citation

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

@article{pan2019pointatrousgraph,
title={PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Atrous Convolution for Point Clouds},
author={Pan, Liang and Chew, Chee-Meng and Lee, Gim Hee},
journal={arXiv preprint arXiv:1907.09798},
year={2019}
}

Our work is inspired by previous work: PointNet, PointNet++ and DGCNN. If you apply their modules, please consider citing their papers also:

@article{qi2016pointnet,
title={PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation},
author={Qi, Charles R and Su, Hao and Mo, Kaichun and Guibas, Leonidas J},
journal={arXiv preprint arXiv:1612.00593},
year={2016}
}
@inproceedings{qi2017pointnet++,
title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space},
author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J},
booktitle={Advances in neural information processing systems},
pages={5099--5108},
year={2017}
}
@article{dgcnn,
title={Dynamic Graph CNN for Learning on Point Clouds},
author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
journal={ACM Transactions on Graphics (TOG)},
year={2019}
}
@article{pan2019pointatrousnet,
title={PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis},
author={Pan, Liang and Wang, Pengfei and Chew, Chee-Meng},
journal={IEEE Robotics and Automation Letters},
volume={4},
number={4},
pages={4035--4041},
year={2019},
publisher={IEEE}
}

License

Our code is released under MIT License.

About

Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points

Topics

atrous-convolutionpointnetsegmentationtensorflow

Resources

Readme
MIT license
Activity

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29 stars

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3 watching

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2 forks
Report repository

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Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
prediction example
Introduction
This work is based on our arXiv tech report.
We propose a deep hierarchical Encoder-Decoder architecture with point atrous convolution to exploit multi-scale edge-aware features in unorganized 3D points.
Experimental results show that our network outperform previous state-of-the-art methods, in 3D object classification, object-part segmentation and semantic segmentation.
In particular, our proposed modules are more efficient (in terms of required training time and memory footprint) than previous networks which heavily rely on neighboring points.
We encourage you to apply our proposed modules for more complicated point cloud applications.
Installation
The code has been tested with Tensorflow 1.4, CUDA 8.0 and Tensorflow 1.12, CUDA 9.0
  1. install required python libs
  2. download correspoinding dataset
  3. compile all the tensorflow ops
Citation
If you find our work useful in your research, please consider citing:
@article{pan2019pointatrousgraph,
title={PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Atrous Convolution for Point Clouds},
author={Pan, Liang and Chew, Chee-Meng and Lee, Gim Hee},
journal={arXiv preprint arXiv:1907.09798},
year={2019}
}

Our work is inspired by previous work: PointNet, PointNet++ and DGCNN.
If you apply their modules, please consider citing their papers also:
@article{qi2016pointnet,
title={PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation},
author={Qi, Charles R and Su, Hao and Mo, Kaichun and Guibas, Leonidas J},
journal={arXiv preprint arXiv:1612.00593},
year={2016}
}
@inproceedings{qi2017pointnet++,
title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space},
author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J},
booktitle={Advances in neural information processing systems},
pages={5099--5108},
year={2017}
}
@article{dgcnn,
title={Dynamic Graph CNN for Learning on Point Clouds},
author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
journal={ACM Transactions on Graphics (TOG)},
year={2019}
}
@article{pan2019pointatrousnet,
title={PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis},
author={Pan, Liang and Wang, Pengfei and Chew, Chee-Meng},
journal={IEEE Robotics and Automation Letters},
volume={4},
number={4},
pages={4035--4041},
year={2019},
publisher={IEEE}
}

License
Our code is released under MIT License.
About
Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
Topics
atrous-convolutionpointnetsegmentationtensorflow
Resources
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MIT license
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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 {
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Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
prediction example
Introduction
This work is based on our arXiv tech report.
We propose a deep hierarchical Encoder-Decoder architecture with point atrous convolution to exploit multi-scale edge-aware features in unorganized 3D points.
Experimental results show that our network outperform previous state-of-the-art methods, in 3D object classification, object-part segmentation and semantic segmentation.
In particular, our proposed modules are more efficient (in terms of required training time and memory footprint) than previous networks which heavily rely on neighboring points.
We encourage you to apply our proposed modules for more complicated point cloud applications.
Installation
The code has been tested with Tensorflow 1.4, CUDA 8.0 and Tensorflow 1.12, CUDA 9.0
  1. install required python libs
  2. download correspoinding dataset
  3. compile all the tensorflow ops
Citation
If you find our work useful in your research, please consider citing:
@article{pan2019pointatrousgraph,
title={PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Atrous Convolution for Point Clouds},
author={Pan, Liang and Chew, Chee-Meng and Lee, Gim Hee},
journal={arXiv preprint arXiv:1907.09798},
year={2019}
}

Our work is inspired by previous work: PointNet, PointNet++ and DGCNN.
If you apply their modules, please consider citing their papers also:
@article{qi2016pointnet,
title={PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation},
author={Qi, Charles R and Su, Hao and Mo, Kaichun and Guibas, Leonidas J},
journal={arXiv preprint arXiv:1612.00593},
year={2016}
}
@inproceedings{qi2017pointnet++,
title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space},
author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J},
booktitle={Advances in neural information processing systems},
pages={5099--5108},
year={2017}
}
@article{dgcnn,
title={Dynamic Graph CNN for Learning on Point Clouds},
author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
journal={ACM Transactions on Graphics (TOG)},
year={2019}
}
@article{pan2019pointatrousnet,
title={PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis},
author={Pan, Liang and Wang, Pengfei and Chew, Chee-Meng},
journal={IEEE Robotics and Automation Letters},
volume={4},
number={4},
pages={4035--4041},
year={2019},
publisher={IEEE}
}

License
Our code is released under MIT License.
About
Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
Topics
atrous-convolutionpointnetsegmentationtensorflow
Resources
Readme
MIT license
Activity
Stars
29 stars
Watchers
3 watching
Forks
2 forks
Report repository
Releases
Packages
Used by
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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");
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
prediction example
Introduction
This work is based on our arXiv tech report.
We propose a deep hierarchical Encoder-Decoder architecture with point atrous convolution to exploit multi-scale edge-aware features in unorganized 3D points.
Experimental results show that our network outperform previous state-of-the-art methods, in 3D object classification, object-part segmentation and semantic segmentation.
In particular, our proposed modules are more efficient (in terms of required training time and memory footprint) than previous networks which heavily rely on neighboring points.
We encourage you to apply our proposed modules for more complicated point cloud applications.
Installation
The code has been tested with Tensorflow 1.4, CUDA 8.0 and Tensorflow 1.12, CUDA 9.0
  1. install required python libs
  2. download correspoinding dataset
  3. compile all the tensorflow ops
Citation
If you find our work useful in your research, please consider citing:
@article{pan2019pointatrousgraph,
title={PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Atrous Convolution for Point Clouds},
author={Pan, Liang and Chew, Chee-Meng and Lee, Gim Hee},
journal={arXiv preprint arXiv:1907.09798},
year={2019}
}

Our work is inspired by previous work: PointNet, PointNet++ and DGCNN.
If you apply their modules, please consider citing their papers also:
@article{qi2016pointnet,
title={PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation},
author={Qi, Charles R and Su, Hao and Mo, Kaichun and Guibas, Leonidas J},
journal={arXiv preprint arXiv:1612.00593},
year={2016}
}
@inproceedings{qi2017pointnet++,
title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space},
author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J},
booktitle={Advances in neural information processing systems},
pages={5099--5108},
year={2017}
}
@article{dgcnn,
title={Dynamic Graph CNN for Learning on Point Clouds},
author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
journal={ACM Transactions on Graphics (TOG)},
year={2019}
}
@article{pan2019pointatrousnet,
title={PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis},
author={Pan, Liang and Wang, Pengfei and Chew, Chee-Meng},
journal={IEEE Robotics and Automation Letters},
volume={4},
number={4},
pages={4035--4041},
year={2019},
publisher={IEEE}
}

License
Our code is released under MIT License.
About
Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
Topics
atrous-convolutionpointnetsegmentationtensorflow
Resources
Readme
MIT license
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3 watching
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2 forks
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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");
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} 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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Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
prediction example
Introduction
This work is based on our arXiv tech report.
We propose a deep hierarchical Encoder-Decoder architecture with point atrous convolution to exploit multi-scale edge-aware features in unorganized 3D points.
Experimental results show that our network outperform previous state-of-the-art methods, in 3D object classification, object-part segmentation and semantic segmentation.
In particular, our proposed modules are more efficient (in terms of required training time and memory footprint) than previous networks which heavily rely on neighboring points.
We encourage you to apply our proposed modules for more complicated point cloud applications.
Installation
The code has been tested with Tensorflow 1.4, CUDA 8.0 and Tensorflow 1.12, CUDA 9.0
  1. install required python libs
  2. download correspoinding dataset
  3. compile all the tensorflow ops
Citation
If you find our work useful in your research, please consider citing:
@article{pan2019pointatrousgraph,
title={PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Atrous Convolution for Point Clouds},
author={Pan, Liang and Chew, Chee-Meng and Lee, Gim Hee},
journal={arXiv preprint arXiv:1907.09798},
year={2019}
}

Our work is inspired by previous work: PointNet, PointNet++ and DGCNN.
If you apply their modules, please consider citing their papers also:
@article{qi2016pointnet,
title={PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation},
author={Qi, Charles R and Su, Hao and Mo, Kaichun and Guibas, Leonidas J},
journal={arXiv preprint arXiv:1612.00593},
year={2016}
}
@inproceedings{qi2017pointnet++,
title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space},
author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J},
booktitle={Advances in neural information processing systems},
pages={5099--5108},
year={2017}
}
@article{dgcnn,
title={Dynamic Graph CNN for Learning on Point Clouds},
author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
journal={ACM Transactions on Graphics (TOG)},
year={2019}
}
@article{pan2019pointatrousnet,
title={PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis},
author={Pan, Liang and Wang, Pengfei and Chew, Chee-Meng},
journal={IEEE Robotics and Automation Letters},
volume={4},
number={4},
pages={4035--4041},
year={2019},
publisher={IEEE}
}

License
Our code is released under MIT License.
About
Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
Topics
atrous-convolutionpointnetsegmentationtensorflow
Resources
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MIT license
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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 {
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var __re = new RegExp('^' + ".*" + '

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Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
prediction example
Introduction
This work is based on our arXiv tech report.
We propose a deep hierarchical Encoder-Decoder architecture with point atrous convolution to exploit multi-scale edge-aware features in unorganized 3D points.
Experimental results show that our network outperform previous state-of-the-art methods, in 3D object classification, object-part segmentation and semantic segmentation.
In particular, our proposed modules are more efficient (in terms of required training time and memory footprint) than previous networks which heavily rely on neighboring points.
We encourage you to apply our proposed modules for more complicated point cloud applications.
Installation
The code has been tested with Tensorflow 1.4, CUDA 8.0 and Tensorflow 1.12, CUDA 9.0
  1. install required python libs
  2. download correspoinding dataset
  3. compile all the tensorflow ops
Citation
If you find our work useful in your research, please consider citing:
@article{pan2019pointatrousgraph,
title={PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Atrous Convolution for Point Clouds},
author={Pan, Liang and Chew, Chee-Meng and Lee, Gim Hee},
journal={arXiv preprint arXiv:1907.09798},
year={2019}
}

Our work is inspired by previous work: PointNet, PointNet++ and DGCNN.
If you apply their modules, please consider citing their papers also:
@article{qi2016pointnet,
title={PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation},
author={Qi, Charles R and Su, Hao and Mo, Kaichun and Guibas, Leonidas J},
journal={arXiv preprint arXiv:1612.00593},
year={2016}
}
@inproceedings{qi2017pointnet++,
title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space},
author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J},
booktitle={Advances in neural information processing systems},
pages={5099--5108},
year={2017}
}
@article{dgcnn,
title={Dynamic Graph CNN for Learning on Point Clouds},
author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
journal={ACM Transactions on Graphics (TOG)},
year={2019}
}
@article{pan2019pointatrousnet,
title={PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis},
author={Pan, Liang and Wang, Pengfei and Chew, Chee-Meng},
journal={IEEE Robotics and Automation Letters},
volume={4},
number={4},
pages={4035--4041},
year={2019},
publisher={IEEE}
}

License
Our code is released under MIT License.
About
Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
Topics
atrous-convolutionpointnetsegmentationtensorflow
Resources
Readme
MIT license
Activity
Stars
29 stars
Watchers
3 watching
Forks
2 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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
prediction example
Introduction
This work is based on our arXiv tech report.
We propose a deep hierarchical Encoder-Decoder architecture with point atrous convolution to exploit multi-scale edge-aware features in unorganized 3D points.
Experimental results show that our network outperform previous state-of-the-art methods, in 3D object classification, object-part segmentation and semantic segmentation.
In particular, our proposed modules are more efficient (in terms of required training time and memory footprint) than previous networks which heavily rely on neighboring points.
We encourage you to apply our proposed modules for more complicated point cloud applications.
Installation
The code has been tested with Tensorflow 1.4, CUDA 8.0 and Tensorflow 1.12, CUDA 9.0
  1. install required python libs
  2. download correspoinding dataset
  3. compile all the tensorflow ops
Citation
If you find our work useful in your research, please consider citing:
@article{pan2019pointatrousgraph,
title={PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Atrous Convolution for Point Clouds},
author={Pan, Liang and Chew, Chee-Meng and Lee, Gim Hee},
journal={arXiv preprint arXiv:1907.09798},
year={2019}
}

Our work is inspired by previous work: PointNet, PointNet++ and DGCNN.
If you apply their modules, please consider citing their papers also:
@article{qi2016pointnet,
title={PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation},
author={Qi, Charles R and Su, Hao and Mo, Kaichun and Guibas, Leonidas J},
journal={arXiv preprint arXiv:1612.00593},
year={2016}
}
@inproceedings{qi2017pointnet++,
title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space},
author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J},
booktitle={Advances in neural information processing systems},
pages={5099--5108},
year={2017}
}
@article{dgcnn,
title={Dynamic Graph CNN for Learning on Point Clouds},
author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
journal={ACM Transactions on Graphics (TOG)},
year={2019}
}
@article{pan2019pointatrousnet,
title={PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis},
author={Pan, Liang and Wang, Pengfei and Chew, Chee-Meng},
journal={IEEE Robotics and Automation Letters},
volume={4},
number={4},
pages={4035--4041},
year={2019},
publisher={IEEE}
}

License
Our code is released under MIT License.
About
Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
Topics
atrous-convolutionpointnetsegmentationtensorflow
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MIT license
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3 watching
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2 forks
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Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
prediction example
Introduction
This work is based on our arXiv tech report.
We propose a deep hierarchical Encoder-Decoder architecture with point atrous convolution to exploit multi-scale edge-aware features in unorganized 3D points.
Experimental results show that our network outperform previous state-of-the-art methods, in 3D object classification, object-part segmentation and semantic segmentation.
In particular, our proposed modules are more efficient (in terms of required training time and memory footprint) than previous networks which heavily rely on neighboring points.
We encourage you to apply our proposed modules for more complicated point cloud applications.
Installation
The code has been tested with Tensorflow 1.4, CUDA 8.0 and Tensorflow 1.12, CUDA 9.0
  1. install required python libs
  2. download correspoinding dataset
  3. compile all the tensorflow ops
Citation
If you find our work useful in your research, please consider citing:
@article{pan2019pointatrousgraph,
title={PointAtrousGraph: Deep Hierarchical Encoder-Decoder with Atrous Convolution for Point Clouds},
author={Pan, Liang and Chew, Chee-Meng and Lee, Gim Hee},
journal={arXiv preprint arXiv:1907.09798},
year={2019}
}

Our work is inspired by previous work: PointNet, PointNet++ and DGCNN.
If you apply their modules, please consider citing their papers also:
@article{qi2016pointnet,
title={PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation},
author={Qi, Charles R and Su, Hao and Mo, Kaichun and Guibas, Leonidas J},
journal={arXiv preprint arXiv:1612.00593},
year={2016}
}
@inproceedings{qi2017pointnet++,
title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space},
author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J},
booktitle={Advances in neural information processing systems},
pages={5099--5108},
year={2017}
}
@article{dgcnn,
title={Dynamic Graph CNN for Learning on Point Clouds},
author={Wang, Yue and Sun, Yongbin and Liu, Ziwei and Sarma, Sanjay E. and Bronstein, Michael M. and Solomon, Justin M.},
journal={ACM Transactions on Graphics (TOG)},
year={2019}
}
@article{pan2019pointatrousnet,
title={PointAtrousNet: Point Atrous Convolution for Point Cloud Analysis},
author={Pan, Liang and Wang, Pengfei and Chew, Chee-Meng},
journal={IEEE Robotics and Automation Letters},
volume={4},
number={4},
pages={4035--4041},
year={2019},
publisher={IEEE}
}

License
Our code is released under MIT License.
About
Deep Hierarchical Encoder-Decoder with Point Atrous Convolution for Unorganized 3D Points
Topics
atrous-convolutionpointnetsegmentationtensorflow
Resources
Readme
MIT license
Activity
Stars
29 stars
Watchers
3 watching
Forks
2 forks
Report repository
Releases
Packages
Used by
Contributors
Languages