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milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing

Static BadgeYouTube BadgeLicense: CC BY-NC 4.0

This work presents milliFlow, a scene flow estimation module to provide an additional layer of point-wise motion information on top of the original mmWave radar point cloud in the conventional mmWave-based human motion sensing pipeline. For technical details, please refer to our paper on ECCV 2024:

milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Fangqiang Ding, Zhen Luo, Peijun Zhao, Chris Xiaoxuan Lu
[arXiv][video][poster]

🔥 News

  • [2024-03-15] Our preprint paper is available on 👉arXiv.
  • [2024-07-01] Our paper is accepted by 🎉ECCV 2024.
  • [2024-09-12] Our presentation video and poster is online. Please check them out 👉video | poster

📝 Abstract

Human motion sensing plays a crucial role in smart systems for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predominantly based on cameras, whose intrusive nature limits their use in smart home applications. To address this, mmWave radars have gained popularity due to their privacy-friendly features. In this work, we propose milliFlow, a novel deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of our method when compared with the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable improvements in human activity recognition and human parsing and support human body part tracking.

🚀 Getting Started

To find out how to run our scodes, please see our intructions in GETTING_STARTED

🔗 Citation

When using the code, model, or dataset, please cite the following paper:

@InProceedings{Ding_2024_ECCV,
author = {Ding, Fangqiang and Luo, Zhen and Zhao, Peijun and Lu, Chris Xiaoxuan},
title = {milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
pages = {202-221},
organization = {Springer}
}

📄 License

Code and Model License

The code and model provided in this repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.

Dataset License

The dataset provided in this repository is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.

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[ECCV 2024] milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing

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milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Static BadgeYouTube BadgeLicense: CC BY-NC 4.0
This work presents milliFlow, a scene flow estimation module to provide an additional layer of point-wise motion information on top of the original mmWave radar point cloud in the conventional mmWave-based human motion sensing pipeline. For technical details, please refer to our paper on ECCV 2024:
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Fangqiang Ding, Zhen Luo, Peijun Zhao, Chris Xiaoxuan Lu
[arXiv][video][poster]
🔥 News
  • [2024-03-15] Our preprint paper is available on 👉arXiv.
  • [2024-07-01] Our paper is accepted by 🎉ECCV 2024.
  • [2024-09-12] Our presentation video and poster is online. Please check them out 👉video | poster
📝 Abstract
Human motion sensing plays a crucial role in smart systems
for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predominantly based on cameras, whose intrusive nature limits their use in smart home applications.
To address this, mmWave radars have gained popularity due to their
privacy-friendly features. In this work, we propose milliFlow, a novel
deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate
level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of
our method when compared with the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable
improvements in human activity recognition and human parsing and
support human body part tracking.
🚀 Getting Started
To find out how to run our scodes, please see our intructions in GETTING_STARTED
🔗 Citation
When using the code, model, or dataset, please cite the following paper:
@InProceedings{Ding_2024_ECCV,
author = {Ding, Fangqiang and Luo, Zhen and Zhao, Peijun and Lu, Chris Xiaoxuan},
title = {milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
pages = {202-221},
organization = {Springer}
}
📄 License
Code and Model License
The code and model provided in this repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
Dataset License
The dataset provided in this repository is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
About
[ECCV 2024] milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
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2 watching
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, 'i');
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milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Static BadgeYouTube BadgeLicense: CC BY-NC 4.0
This work presents milliFlow, a scene flow estimation module to provide an additional layer of point-wise motion information on top of the original mmWave radar point cloud in the conventional mmWave-based human motion sensing pipeline. For technical details, please refer to our paper on ECCV 2024:
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Fangqiang Ding, Zhen Luo, Peijun Zhao, Chris Xiaoxuan Lu
[arXiv][video][poster]
🔥 News
  • [2024-03-15] Our preprint paper is available on 👉arXiv.
  • [2024-07-01] Our paper is accepted by 🎉ECCV 2024.
  • [2024-09-12] Our presentation video and poster is online. Please check them out 👉video | poster
📝 Abstract
Human motion sensing plays a crucial role in smart systems
for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predominantly based on cameras, whose intrusive nature limits their use in smart home applications.
To address this, mmWave radars have gained popularity due to their
privacy-friendly features. In this work, we propose milliFlow, a novel
deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate
level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of
our method when compared with the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable
improvements in human activity recognition and human parsing and
support human body part tracking.
🚀 Getting Started
To find out how to run our scodes, please see our intructions in GETTING_STARTED
🔗 Citation
When using the code, model, or dataset, please cite the following paper:
@InProceedings{Ding_2024_ECCV,
author = {Ding, Fangqiang and Luo, Zhen and Zhao, Peijun and Lu, Chris Xiaoxuan},
title = {milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
pages = {202-221},
organization = {Springer}
}
📄 License
Code and Model License
The code and model provided in this repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
Dataset License
The dataset provided in this repository is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
About
[ECCV 2024] milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
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2 watching
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Static BadgeYouTube BadgeLicense: CC BY-NC 4.0
This work presents milliFlow, a scene flow estimation module to provide an additional layer of point-wise motion information on top of the original mmWave radar point cloud in the conventional mmWave-based human motion sensing pipeline. For technical details, please refer to our paper on ECCV 2024:
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Fangqiang Ding, Zhen Luo, Peijun Zhao, Chris Xiaoxuan Lu
[arXiv][video][poster]
🔥 News
  • [2024-03-15] Our preprint paper is available on 👉arXiv.
  • [2024-07-01] Our paper is accepted by 🎉ECCV 2024.
  • [2024-09-12] Our presentation video and poster is online. Please check them out 👉video | poster
📝 Abstract
Human motion sensing plays a crucial role in smart systems
for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predominantly based on cameras, whose intrusive nature limits their use in smart home applications.
To address this, mmWave radars have gained popularity due to their
privacy-friendly features. In this work, we propose milliFlow, a novel
deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate
level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of
our method when compared with the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable
improvements in human activity recognition and human parsing and
support human body part tracking.
🚀 Getting Started
To find out how to run our scodes, please see our intructions in GETTING_STARTED
🔗 Citation
When using the code, model, or dataset, please cite the following paper:
@InProceedings{Ding_2024_ECCV,
author = {Ding, Fangqiang and Luo, Zhen and Zhao, Peijun and Lu, Chris Xiaoxuan},
title = {milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
pages = {202-221},
organization = {Springer}
}
📄 License
Code and Model License
The code and model provided in this repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
Dataset License
The dataset provided in this repository is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
About
[ECCV 2024] milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
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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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})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Static BadgeYouTube BadgeLicense: CC BY-NC 4.0
This work presents milliFlow, a scene flow estimation module to provide an additional layer of point-wise motion information on top of the original mmWave radar point cloud in the conventional mmWave-based human motion sensing pipeline. For technical details, please refer to our paper on ECCV 2024:
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Fangqiang Ding, Zhen Luo, Peijun Zhao, Chris Xiaoxuan Lu
[arXiv][video][poster]
🔥 News
  • [2024-03-15] Our preprint paper is available on 👉arXiv.
  • [2024-07-01] Our paper is accepted by 🎉ECCV 2024.
  • [2024-09-12] Our presentation video and poster is online. Please check them out 👉video | poster
📝 Abstract
Human motion sensing plays a crucial role in smart systems
for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predominantly based on cameras, whose intrusive nature limits their use in smart home applications.
To address this, mmWave radars have gained popularity due to their
privacy-friendly features. In this work, we propose milliFlow, a novel
deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate
level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of
our method when compared with the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable
improvements in human activity recognition and human parsing and
support human body part tracking.
🚀 Getting Started
To find out how to run our scodes, please see our intructions in GETTING_STARTED
🔗 Citation
When using the code, model, or dataset, please cite the following paper:
@InProceedings{Ding_2024_ECCV,
author = {Ding, Fangqiang and Luo, Zhen and Zhao, Peijun and Lu, Chris Xiaoxuan},
title = {milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
pages = {202-221},
organization = {Springer}
}
📄 License
Code and Model License
The code and model provided in this repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
Dataset License
The dataset provided in this repository is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
About
[ECCV 2024] milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
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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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})();
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var __m = "*";
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milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Static BadgeYouTube BadgeLicense: CC BY-NC 4.0
This work presents milliFlow, a scene flow estimation module to provide an additional layer of point-wise motion information on top of the original mmWave radar point cloud in the conventional mmWave-based human motion sensing pipeline. For technical details, please refer to our paper on ECCV 2024:
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Fangqiang Ding, Zhen Luo, Peijun Zhao, Chris Xiaoxuan Lu
[arXiv][video][poster]
🔥 News
  • [2024-03-15] Our preprint paper is available on 👉arXiv.
  • [2024-07-01] Our paper is accepted by 🎉ECCV 2024.
  • [2024-09-12] Our presentation video and poster is online. Please check them out 👉video | poster
📝 Abstract
Human motion sensing plays a crucial role in smart systems
for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predominantly based on cameras, whose intrusive nature limits their use in smart home applications.
To address this, mmWave radars have gained popularity due to their
privacy-friendly features. In this work, we propose milliFlow, a novel
deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate
level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of
our method when compared with the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable
improvements in human activity recognition and human parsing and
support human body part tracking.
🚀 Getting Started
To find out how to run our scodes, please see our intructions in GETTING_STARTED
🔗 Citation
When using the code, model, or dataset, please cite the following paper:
@InProceedings{Ding_2024_ECCV,
author = {Ding, Fangqiang and Luo, Zhen and Zhao, Peijun and Lu, Chris Xiaoxuan},
title = {milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
pages = {202-221},
organization = {Springer}
}
📄 License
Code and Model License
The code and model provided in this repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
Dataset License
The dataset provided in this repository is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
About
[ECCV 2024] milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Resources
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Stars
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, '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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milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Static BadgeYouTube BadgeLicense: CC BY-NC 4.0
This work presents milliFlow, a scene flow estimation module to provide an additional layer of point-wise motion information on top of the original mmWave radar point cloud in the conventional mmWave-based human motion sensing pipeline. For technical details, please refer to our paper on ECCV 2024:
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Fangqiang Ding, Zhen Luo, Peijun Zhao, Chris Xiaoxuan Lu
[arXiv][video][poster]
🔥 News
  • [2024-03-15] Our preprint paper is available on 👉arXiv.
  • [2024-07-01] Our paper is accepted by 🎉ECCV 2024.
  • [2024-09-12] Our presentation video and poster is online. Please check them out 👉video | poster
📝 Abstract
Human motion sensing plays a crucial role in smart systems
for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predominantly based on cameras, whose intrusive nature limits their use in smart home applications.
To address this, mmWave radars have gained popularity due to their
privacy-friendly features. In this work, we propose milliFlow, a novel
deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate
level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of
our method when compared with the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable
improvements in human activity recognition and human parsing and
support human body part tracking.
🚀 Getting Started
To find out how to run our scodes, please see our intructions in GETTING_STARTED
🔗 Citation
When using the code, model, or dataset, please cite the following paper:
@InProceedings{Ding_2024_ECCV,
author = {Ding, Fangqiang and Luo, Zhen and Zhao, Peijun and Lu, Chris Xiaoxuan},
title = {milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
pages = {202-221},
organization = {Springer}
}
📄 License
Code and Model License
The code and model provided in this repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
Dataset License
The dataset provided in this repository is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
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milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Static BadgeYouTube BadgeLicense: CC BY-NC 4.0
This work presents milliFlow, a scene flow estimation module to provide an additional layer of point-wise motion information on top of the original mmWave radar point cloud in the conventional mmWave-based human motion sensing pipeline. For technical details, please refer to our paper on ECCV 2024:
milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Fangqiang Ding, Zhen Luo, Peijun Zhao, Chris Xiaoxuan Lu
[arXiv][video][poster]
🔥 News
  • [2024-03-15] Our preprint paper is available on 👉arXiv.
  • [2024-07-01] Our paper is accepted by 🎉ECCV 2024.
  • [2024-09-12] Our presentation video and poster is online. Please check them out 👉video | poster
📝 Abstract
Human motion sensing plays a crucial role in smart systems
for decision-making, user interaction, and personalized services. Extensive research that has been conducted is predominantly based on cameras, whose intrusive nature limits their use in smart home applications.
To address this, mmWave radars have gained popularity due to their
privacy-friendly features. In this work, we propose milliFlow, a novel
deep learning approach to estimate scene flow as complementary motion information for mmWave point cloud, serving as an intermediate
level of features and directly benefiting downstream human motion sensing tasks. Experimental results demonstrate the superior performance of
our method when compared with the competing approaches. Furthermore, by incorporating scene flow information, we achieve remarkable
improvements in human activity recognition and human parsing and
support human body part tracking.
🚀 Getting Started
To find out how to run our scodes, please see our intructions in GETTING_STARTED
🔗 Citation
When using the code, model, or dataset, please cite the following paper:
@InProceedings{Ding_2024_ECCV,
author = {Ding, Fangqiang and Luo, Zhen and Zhao, Peijun and Lu, Chris Xiaoxuan},
title = {milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2024},
pages = {202-221},
organization = {Springer}
}
📄 License
Code and Model License
The code and model provided in this repository are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
Dataset License
The dataset provided in this repository is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for details.
About
[ECCV 2024] milliFlow: Scene Flow Estimation on mmWave Radar Point Cloud for Human Motion Sensing
Resources
Readme
License
Activity
Stars
53 stars
Watchers
2 watching
Forks
5 forks
Report repository
Releases
Packages
Used by
Contributors
Languages