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MapFM

Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning

Leonid Ivanov1, Vasily Yuryev1 and Dmitry Yudin1,2

1 Intelligent Transport Lab, MIPT, 2 AIRI

ArXiv Preprint (arXiv 2506.15313)

News

  • 16 Oct 2025: MapFM is released!
  • 14 July 2025: MapFM is accepted by HAIS 2025! 🎉
  • 8 Jun 2025: We released preprint on Arxiv. Code/Models are coming soon. 🚀

Introduction

In autonomous driving, high-definition (HD) maps and semantic maps in bird's-eye view (BEV) are essential for accurate localization, planning, and decision-making. This paper introduces an enhanced End-to-End model named MapFM for online vectorized HD map generation. We show significantly boost feature representation quality by incorporating powerful foundation model for encoding camera images. To further enrich the model's understanding of the environment and improve prediction quality, we integrate auxiliary prediction heads for semantic segmentation in the BEV representation. This multi-task learning approach provides richer contextual supervision, leading to a more comprehensive scene representation and ultimately resulting in higher accuracy and improved quality of the predicted vectorized HD maps. We have an increase in mean average precision (mAP) compared to baseline on the nuScenes dataset.

method

TODO

  • Release the code.

  • Release pre-trained models.

Getting Started

These settings keep the same as MapTRv2

Acknowledgements

MapFM is based on mmdetection3d. It is also greatly inspired by the following outstanding contributions to the open-source community: MapQR, Cross View Transformers, Hugging Face.

Citation

If the paper and code help your research, please kindly cite:

@inproceedings{ivanov2025mapfm,
title={Mapfm: Foundation model-driven hd mapping with multi-task contextual learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
booktitle={International Conference on Hybrid Artificial Intelligence Systems},
pages={28--40},
year={2025},
organization={Springer}
}
@misc{ivanov2025mapfmfoundationmodeldrivenhd,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning}, author={Leonid Ivanov and Vasily Yuryev and Dmitry Yudin},
year={2025},
eprint={2506.15313},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.15313}, }
@article{ivanov2025mapfm,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
journal={arXiv preprint arXiv:2506.15313},
year={2025}
}

About

[HAIS 2025] MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning

arxiv.org/abs/2506.15313

Topics

autonomous-drivingdinov2hd-maphd-map-construction

Resources

Readme
MIT license
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13 stars

Watchers

2 watching

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2 forks
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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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MapFM
Foundation Model-Driven HD Mapping
with Multi-Task Contextual Learning
Leonid Ivanov1, Vasily Yuryev1 and Dmitry Yudin1,2
1 Intelligent Transport Lab, MIPT, 2 AIRI
ArXiv Preprint (arXiv 2506.15313)
News
  • 16 Oct 2025: MapFM is released!
  • 14 July 2025: MapFM is accepted by HAIS 2025! 🎉
  • 8 Jun 2025: We released preprint on Arxiv. Code/Models are coming soon. 🚀
Introduction
In autonomous driving, high-definition (HD) maps and semantic maps in bird's-eye view (BEV) are essential for accurate localization, planning, and decision-making. This paper introduces an enhanced End-to-End model named MapFM for online vectorized HD map generation. We show significantly boost feature representation quality by incorporating powerful foundation model for encoding camera images. To further enrich the model's understanding of the environment and improve prediction quality, we integrate auxiliary prediction heads for semantic segmentation in the BEV representation. This multi-task learning approach provides richer contextual supervision, leading to a more comprehensive scene representation and ultimately resulting in higher accuracy and improved quality of the predicted vectorized HD maps. We have an increase in mean average precision (mAP) compared to baseline on the nuScenes dataset.
method
TODO
  •  Release the code.
  •  Release pre-trained models.
Getting Started
These settings keep the same as MapTRv2
Acknowledgements
MapFM is based on mmdetection3d. It is also greatly inspired by the following outstanding contributions to the open-source community: MapQR, Cross View Transformers, Hugging Face.
Citation
If the paper and code help your research, please kindly cite:
@inproceedings{ivanov2025mapfm,
title={Mapfm: Foundation model-driven hd mapping with multi-task contextual learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
booktitle={International Conference on Hybrid Artificial Intelligence Systems},
pages={28--40},
year={2025},
organization={Springer}
}
@misc{ivanov2025mapfmfoundationmodeldrivenhd,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning}, author={Leonid Ivanov and Vasily Yuryev and Dmitry Yudin},
year={2025},
eprint={2506.15313},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.15313}, }
@article{ivanov2025mapfm,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
journal={arXiv preprint arXiv:2506.15313},
year={2025}
}
About
[HAIS 2025] MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning
arxiv.org/abs/2506.15313
Topics
autonomous-drivingdinov2hd-maphd-map-construction
Resources
Readme
MIT license
Activity
Stars
13 stars
Watchers
2 watching
Forks
2 forks
Report repository
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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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MapFM
Foundation Model-Driven HD Mapping
with Multi-Task Contextual Learning
Leonid Ivanov1, Vasily Yuryev1 and Dmitry Yudin1,2
1 Intelligent Transport Lab, MIPT, 2 AIRI
ArXiv Preprint (arXiv 2506.15313)
News
  • 16 Oct 2025: MapFM is released!
  • 14 July 2025: MapFM is accepted by HAIS 2025! 🎉
  • 8 Jun 2025: We released preprint on Arxiv. Code/Models are coming soon. 🚀
Introduction
In autonomous driving, high-definition (HD) maps and semantic maps in bird's-eye view (BEV) are essential for accurate localization, planning, and decision-making. This paper introduces an enhanced End-to-End model named MapFM for online vectorized HD map generation. We show significantly boost feature representation quality by incorporating powerful foundation model for encoding camera images. To further enrich the model's understanding of the environment and improve prediction quality, we integrate auxiliary prediction heads for semantic segmentation in the BEV representation. This multi-task learning approach provides richer contextual supervision, leading to a more comprehensive scene representation and ultimately resulting in higher accuracy and improved quality of the predicted vectorized HD maps. We have an increase in mean average precision (mAP) compared to baseline on the nuScenes dataset.
method
TODO
  •  Release the code.
  •  Release pre-trained models.
Getting Started
These settings keep the same as MapTRv2
Acknowledgements
MapFM is based on mmdetection3d. It is also greatly inspired by the following outstanding contributions to the open-source community: MapQR, Cross View Transformers, Hugging Face.
Citation
If the paper and code help your research, please kindly cite:
@inproceedings{ivanov2025mapfm,
title={Mapfm: Foundation model-driven hd mapping with multi-task contextual learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
booktitle={International Conference on Hybrid Artificial Intelligence Systems},
pages={28--40},
year={2025},
organization={Springer}
}
@misc{ivanov2025mapfmfoundationmodeldrivenhd,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning}, author={Leonid Ivanov and Vasily Yuryev and Dmitry Yudin},
year={2025},
eprint={2506.15313},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.15313}, }
@article{ivanov2025mapfm,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
journal={arXiv preprint arXiv:2506.15313},
year={2025}
}
About
[HAIS 2025] MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning
arxiv.org/abs/2506.15313
Topics
autonomous-drivingdinov2hd-maphd-map-construction
Resources
Readme
MIT license
Activity
Stars
13 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages

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

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Repository files navigation
MapFM
Foundation Model-Driven HD Mapping
with Multi-Task Contextual Learning
Leonid Ivanov1, Vasily Yuryev1 and Dmitry Yudin1,2
1 Intelligent Transport Lab, MIPT, 2 AIRI
ArXiv Preprint (arXiv 2506.15313)
News
  • 16 Oct 2025: MapFM is released!
  • 14 July 2025: MapFM is accepted by HAIS 2025! 🎉
  • 8 Jun 2025: We released preprint on Arxiv. Code/Models are coming soon. 🚀
Introduction
In autonomous driving, high-definition (HD) maps and semantic maps in bird's-eye view (BEV) are essential for accurate localization, planning, and decision-making. This paper introduces an enhanced End-to-End model named MapFM for online vectorized HD map generation. We show significantly boost feature representation quality by incorporating powerful foundation model for encoding camera images. To further enrich the model's understanding of the environment and improve prediction quality, we integrate auxiliary prediction heads for semantic segmentation in the BEV representation. This multi-task learning approach provides richer contextual supervision, leading to a more comprehensive scene representation and ultimately resulting in higher accuracy and improved quality of the predicted vectorized HD maps. We have an increase in mean average precision (mAP) compared to baseline on the nuScenes dataset.
method
TODO
  •  Release the code.
  •  Release pre-trained models.
Getting Started
These settings keep the same as MapTRv2
Acknowledgements
MapFM is based on mmdetection3d. It is also greatly inspired by the following outstanding contributions to the open-source community: MapQR, Cross View Transformers, Hugging Face.
Citation
If the paper and code help your research, please kindly cite:
@inproceedings{ivanov2025mapfm,
title={Mapfm: Foundation model-driven hd mapping with multi-task contextual learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
booktitle={International Conference on Hybrid Artificial Intelligence Systems},
pages={28--40},
year={2025},
organization={Springer}
}
@misc{ivanov2025mapfmfoundationmodeldrivenhd,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning}, author={Leonid Ivanov and Vasily Yuryev and Dmitry Yudin},
year={2025},
eprint={2506.15313},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.15313}, }
@article{ivanov2025mapfm,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
journal={arXiv preprint arXiv:2506.15313},
year={2025}
}
About
[HAIS 2025] MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning
arxiv.org/abs/2506.15313
Topics
autonomous-drivingdinov2hd-maphd-map-construction
Resources
Readme
MIT license
Activity
Stars
13 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages

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

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Repository files navigation
MapFM
Foundation Model-Driven HD Mapping
with Multi-Task Contextual Learning
Leonid Ivanov1, Vasily Yuryev1 and Dmitry Yudin1,2
1 Intelligent Transport Lab, MIPT, 2 AIRI
ArXiv Preprint (arXiv 2506.15313)
News
  • 16 Oct 2025: MapFM is released!
  • 14 July 2025: MapFM is accepted by HAIS 2025! 🎉
  • 8 Jun 2025: We released preprint on Arxiv. Code/Models are coming soon. 🚀
Introduction
In autonomous driving, high-definition (HD) maps and semantic maps in bird's-eye view (BEV) are essential for accurate localization, planning, and decision-making. This paper introduces an enhanced End-to-End model named MapFM for online vectorized HD map generation. We show significantly boost feature representation quality by incorporating powerful foundation model for encoding camera images. To further enrich the model's understanding of the environment and improve prediction quality, we integrate auxiliary prediction heads for semantic segmentation in the BEV representation. This multi-task learning approach provides richer contextual supervision, leading to a more comprehensive scene representation and ultimately resulting in higher accuracy and improved quality of the predicted vectorized HD maps. We have an increase in mean average precision (mAP) compared to baseline on the nuScenes dataset.
method
TODO
  •  Release the code.
  •  Release pre-trained models.
Getting Started
These settings keep the same as MapTRv2
Acknowledgements
MapFM is based on mmdetection3d. It is also greatly inspired by the following outstanding contributions to the open-source community: MapQR, Cross View Transformers, Hugging Face.
Citation
If the paper and code help your research, please kindly cite:
@inproceedings{ivanov2025mapfm,
title={Mapfm: Foundation model-driven hd mapping with multi-task contextual learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
booktitle={International Conference on Hybrid Artificial Intelligence Systems},
pages={28--40},
year={2025},
organization={Springer}
}
@misc{ivanov2025mapfmfoundationmodeldrivenhd,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning}, author={Leonid Ivanov and Vasily Yuryev and Dmitry Yudin},
year={2025},
eprint={2506.15313},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.15313}, }
@article{ivanov2025mapfm,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
journal={arXiv preprint arXiv:2506.15313},
year={2025}
}
About
[HAIS 2025] MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning
arxiv.org/abs/2506.15313
Topics
autonomous-drivingdinov2hd-maphd-map-construction
Resources
Readme
MIT license
Activity
Stars
13 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
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Contributors
Languages

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

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Repository files navigation
MapFM
Foundation Model-Driven HD Mapping
with Multi-Task Contextual Learning
Leonid Ivanov1, Vasily Yuryev1 and Dmitry Yudin1,2
1 Intelligent Transport Lab, MIPT, 2 AIRI
ArXiv Preprint (arXiv 2506.15313)
News
  • 16 Oct 2025: MapFM is released!
  • 14 July 2025: MapFM is accepted by HAIS 2025! 🎉
  • 8 Jun 2025: We released preprint on Arxiv. Code/Models are coming soon. 🚀
Introduction
In autonomous driving, high-definition (HD) maps and semantic maps in bird's-eye view (BEV) are essential for accurate localization, planning, and decision-making. This paper introduces an enhanced End-to-End model named MapFM for online vectorized HD map generation. We show significantly boost feature representation quality by incorporating powerful foundation model for encoding camera images. To further enrich the model's understanding of the environment and improve prediction quality, we integrate auxiliary prediction heads for semantic segmentation in the BEV representation. This multi-task learning approach provides richer contextual supervision, leading to a more comprehensive scene representation and ultimately resulting in higher accuracy and improved quality of the predicted vectorized HD maps. We have an increase in mean average precision (mAP) compared to baseline on the nuScenes dataset.
method
TODO
  •  Release the code.
  •  Release pre-trained models.
Getting Started
These settings keep the same as MapTRv2
Acknowledgements
MapFM is based on mmdetection3d. It is also greatly inspired by the following outstanding contributions to the open-source community: MapQR, Cross View Transformers, Hugging Face.
Citation
If the paper and code help your research, please kindly cite:
@inproceedings{ivanov2025mapfm,
title={Mapfm: Foundation model-driven hd mapping with multi-task contextual learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
booktitle={International Conference on Hybrid Artificial Intelligence Systems},
pages={28--40},
year={2025},
organization={Springer}
}
@misc{ivanov2025mapfmfoundationmodeldrivenhd,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning}, author={Leonid Ivanov and Vasily Yuryev and Dmitry Yudin},
year={2025},
eprint={2506.15313},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.15313}, }
@article{ivanov2025mapfm,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
journal={arXiv preprint arXiv:2506.15313},
year={2025}
}
About
[HAIS 2025] MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning
arxiv.org/abs/2506.15313
Topics
autonomous-drivingdinov2hd-maphd-map-construction
Resources
Readme
MIT license
Activity
Stars
13 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
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var __m = "*";
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MapFM
Foundation Model-Driven HD Mapping
with Multi-Task Contextual Learning
Leonid Ivanov1, Vasily Yuryev1 and Dmitry Yudin1,2
1 Intelligent Transport Lab, MIPT, 2 AIRI
ArXiv Preprint (arXiv 2506.15313)
News
  • 16 Oct 2025: MapFM is released!
  • 14 July 2025: MapFM is accepted by HAIS 2025! 🎉
  • 8 Jun 2025: We released preprint on Arxiv. Code/Models are coming soon. 🚀
Introduction
In autonomous driving, high-definition (HD) maps and semantic maps in bird's-eye view (BEV) are essential for accurate localization, planning, and decision-making. This paper introduces an enhanced End-to-End model named MapFM for online vectorized HD map generation. We show significantly boost feature representation quality by incorporating powerful foundation model for encoding camera images. To further enrich the model's understanding of the environment and improve prediction quality, we integrate auxiliary prediction heads for semantic segmentation in the BEV representation. This multi-task learning approach provides richer contextual supervision, leading to a more comprehensive scene representation and ultimately resulting in higher accuracy and improved quality of the predicted vectorized HD maps. We have an increase in mean average precision (mAP) compared to baseline on the nuScenes dataset.
method
TODO
  •  Release the code.
  •  Release pre-trained models.
Getting Started
These settings keep the same as MapTRv2
Acknowledgements
MapFM is based on mmdetection3d. It is also greatly inspired by the following outstanding contributions to the open-source community: MapQR, Cross View Transformers, Hugging Face.
Citation
If the paper and code help your research, please kindly cite:
@inproceedings{ivanov2025mapfm,
title={Mapfm: Foundation model-driven hd mapping with multi-task contextual learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
booktitle={International Conference on Hybrid Artificial Intelligence Systems},
pages={28--40},
year={2025},
organization={Springer}
}
@misc{ivanov2025mapfmfoundationmodeldrivenhd,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning}, author={Leonid Ivanov and Vasily Yuryev and Dmitry Yudin},
year={2025},
eprint={2506.15313},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.15313}, }
@article{ivanov2025mapfm,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
journal={arXiv preprint arXiv:2506.15313},
year={2025}
}
About
[HAIS 2025] MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning
arxiv.org/abs/2506.15313
Topics
autonomous-drivingdinov2hd-maphd-map-construction
Resources
Readme
MIT license
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13 stars
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2 watching
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})();
})();

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Repository files navigation
MapFM
Foundation Model-Driven HD Mapping
with Multi-Task Contextual Learning
Leonid Ivanov1, Vasily Yuryev1 and Dmitry Yudin1,2
1 Intelligent Transport Lab, MIPT, 2 AIRI
ArXiv Preprint (arXiv 2506.15313)
News
  • 16 Oct 2025: MapFM is released!
  • 14 July 2025: MapFM is accepted by HAIS 2025! 🎉
  • 8 Jun 2025: We released preprint on Arxiv. Code/Models are coming soon. 🚀
Introduction
In autonomous driving, high-definition (HD) maps and semantic maps in bird's-eye view (BEV) are essential for accurate localization, planning, and decision-making. This paper introduces an enhanced End-to-End model named MapFM for online vectorized HD map generation. We show significantly boost feature representation quality by incorporating powerful foundation model for encoding camera images. To further enrich the model's understanding of the environment and improve prediction quality, we integrate auxiliary prediction heads for semantic segmentation in the BEV representation. This multi-task learning approach provides richer contextual supervision, leading to a more comprehensive scene representation and ultimately resulting in higher accuracy and improved quality of the predicted vectorized HD maps. We have an increase in mean average precision (mAP) compared to baseline on the nuScenes dataset.
method
TODO
  •  Release the code.
  •  Release pre-trained models.
Getting Started
These settings keep the same as MapTRv2
Acknowledgements
MapFM is based on mmdetection3d. It is also greatly inspired by the following outstanding contributions to the open-source community: MapQR, Cross View Transformers, Hugging Face.
Citation
If the paper and code help your research, please kindly cite:
@inproceedings{ivanov2025mapfm,
title={Mapfm: Foundation model-driven hd mapping with multi-task contextual learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
booktitle={International Conference on Hybrid Artificial Intelligence Systems},
pages={28--40},
year={2025},
organization={Springer}
}
@misc{ivanov2025mapfmfoundationmodeldrivenhd,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning}, author={Leonid Ivanov and Vasily Yuryev and Dmitry Yudin},
year={2025},
eprint={2506.15313},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.15313}, }
@article{ivanov2025mapfm,
title={MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning},
author={Ivanov, Leonid and Yuryev, Vasily and Yudin, Dmitry},
journal={arXiv preprint arXiv:2506.15313},
year={2025}
}
About
[HAIS 2025] MapFM: Foundation Model-Driven HD Mapping with Multi-Task Contextual Learning
arxiv.org/abs/2506.15313
Topics
autonomous-drivingdinov2hd-maphd-map-construction
Resources
Readme
MIT license
Activity
Stars
13 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages