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MapQR

Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction

Zihao Liu1*, Xiaoyu Zhang2*, Guangwei Liu3*, Ji Zhao3#, Ningyi Xu1#

1 Shanghai Jiao Tong University, 2 The Chinese University of Hong Kong, 3 Huixi Technology

*Equal contribution. #Corresponding author.

ArXiv Preprint (arXiv 2402.17430)

Accepted by ECCV 2024

🔥 News

  • 2024.08: 🎉🎉 Our new work HRMapNet is now released, it utilizes historical information to enhance HD map construction!
  • 2024.07: 🎉🎉 MapQR is accepted in ECCV 2024!

Overview

pipeline This project introduces MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps. Although the map construction is essentially a point set prediction task, MapQR utilizes instance queries rather than point queries. These instance queries are scattered for the prediction of point sets and subsequently gathered for the final matching. This query design, called the scatter-and-gather query, shares content information in the same map element and avoids possible inconsistency of content information in point queries. We further exploit prior information to enhance an instance query by adding positional information embedded from their reference points. Together with a simple and effective improvement of a BEV encoder, the proposed MapQR achieves the best mean average precision (mAP) and maintains good efficiency on both nuScenes and Argoverse 2.

The main contribution is the proposed scatter-and-gather query, illustrated in the following figure.

Models

nuScenes dataset

MethodBackboneEpochmAP1mAP2ConfigDownload
MapQRR502443.366.4configmodel
MapQRR5011050.572.6configmodel

Argoverse 2 dataset

MethodBackboneEpochdimmAP1mAP2ConfigDownload
MapQRR506244.868.1configmodel
MapQRR506341.265.4configmodel
  • mAP1 is measured under the thresholds { 0.2, 0.5, 1.0 }
  • mAP2 is measured under the thresholds { 0.5, 1.0, 1.5 }

Getting Started

These settings keep the same as MapTRv2

Note

If you meet nan during training, you could comment out this line:

MapQR/projects/mmdet3d_plugin/bevformer/modules/encoder.py

Line 149 in d05554c

@auto_fp16() # This may cause 'grad_norm: nan'

Acknowledgements

MapQR is mainly based on MapTRv2.

It is also greatly inspired by the following outstanding contributions to the open-source community: BEVFormer, GKT, ConditionalDETR, DAB-DETR.

Citation

If you find MapQR is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.

@inproceedings{liu2024leveraging,
title={Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction},
author={Liu, Zihao and Zhang, Xiaoyu and Liu, Guangwei and Zhao, Ji and Xu, Ningyi},
booktitle={European Conference on Computer Vision},
year={2024}
}

About

[ECCV 2024] This is the official implementation of MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.

arxiv.org/abs/2402.17430

Topics

autonomous-drivingbevdeep-learningdetreccv2024hd-map-constructiononline-hdmap-constructionself-driving-carvectorized-hdmap

Resources

Readme
MIT license
Activity
Custom properties

Stars

230 stars

Watchers

6 watching

Forks

21 forks
Report repository

Used by

Contributors

Languages

, '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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MapQR
Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction
Zihao Liu1*,
Xiaoyu Zhang2*,
Guangwei Liu3*,
Ji Zhao3#,
Ningyi Xu1#
1 Shanghai Jiao Tong University,
2 The Chinese University of Hong Kong,
3 Huixi Technology
*Equal contribution. #Corresponding author.
ArXiv Preprint (arXiv 2402.17430)
Accepted by ECCV 2024
🔥 News
  • 2024.08: 🎉🎉 Our new work HRMapNet is now released, it utilizes historical information to enhance HD map construction!
  • 2024.07: 🎉🎉 MapQR is accepted in ECCV 2024!
Overview
pipeline
This project introduces MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
Although the map construction is essentially a point set prediction task, MapQR utilizes instance queries rather than point queries.
These instance queries are scattered for the prediction of point sets and subsequently gathered for the final matching.
This query design, called the scatter-and-gather query, shares content information in the same map element and avoids possible inconsistency
of content information in point queries.
We further exploit prior information to enhance an instance query by adding positional information embedded from their reference points.
Together with a simple and effective improvement of a BEV encoder, the proposed MapQR achieves the best mean average precision (mAP) and maintains good efficiency on both nuScenes and Argoverse 2.
The main contribution is the proposed scatter-and-gather query, illustrated in the following figure.
Models
nuScenes dataset
MethodBackboneEpochmAP1mAP2ConfigDownload
MapQRR502443.366.4configmodel
MapQRR5011050.572.6configmodel
Argoverse 2 dataset
MethodBackboneEpochdimmAP1mAP2ConfigDownload
MapQRR506244.868.1configmodel
MapQRR506341.265.4configmodel
  • mAP1 is measured under the thresholds { 0.2, 0.5, 1.0 }
  • mAP2 is measured under the thresholds { 0.5, 1.0, 1.5 }
Getting Started
These settings keep the same as MapTRv2
Note
If you meet nan during training, you could comment out this line:

MapQR/projects/mmdet3d_plugin/bevformer/modules/encoder.py

Line 149
in
d05554c
@auto_fp16() # This may cause 'grad_norm: nan'
Acknowledgements
MapQR is mainly based on MapTRv2.
It is also greatly inspired by the following outstanding contributions to the open-source community:
BEVFormer,
GKT,
ConditionalDETR,
DAB-DETR.
Citation
If you find MapQR is useful in your research or applications,
please consider giving us a star 🌟 and citing it by the following BibTeX entry.
@inproceedings{liu2024leveraging,
title={Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction},
author={Liu, Zihao and Zhang, Xiaoyu and Liu, Guangwei and Zhao, Ji and Xu, Ningyi},
booktitle={European Conference on Computer Vision},
year={2024}
}
About
[ECCV 2024] This is the official implementation of MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
arxiv.org/abs/2402.17430
Topics
autonomous-drivingbevdeep-learningdetreccv2024hd-map-constructiononline-hdmap-constructionself-driving-carvectorized-hdmap
Resources
Readme
MIT license
Activity
Custom properties
Stars
230 stars
Watchers
6 watching
Forks
21 forks
Report repository
Used by
Contributors
Languages

, '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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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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MapQR
Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction
Zihao Liu1*,
Xiaoyu Zhang2*,
Guangwei Liu3*,
Ji Zhao3#,
Ningyi Xu1#
1 Shanghai Jiao Tong University,
2 The Chinese University of Hong Kong,
3 Huixi Technology
*Equal contribution. #Corresponding author.
ArXiv Preprint (arXiv 2402.17430)
Accepted by ECCV 2024
🔥 News
  • 2024.08: 🎉🎉 Our new work HRMapNet is now released, it utilizes historical information to enhance HD map construction!
  • 2024.07: 🎉🎉 MapQR is accepted in ECCV 2024!
Overview
pipeline
This project introduces MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
Although the map construction is essentially a point set prediction task, MapQR utilizes instance queries rather than point queries.
These instance queries are scattered for the prediction of point sets and subsequently gathered for the final matching.
This query design, called the scatter-and-gather query, shares content information in the same map element and avoids possible inconsistency
of content information in point queries.
We further exploit prior information to enhance an instance query by adding positional information embedded from their reference points.
Together with a simple and effective improvement of a BEV encoder, the proposed MapQR achieves the best mean average precision (mAP) and maintains good efficiency on both nuScenes and Argoverse 2.
The main contribution is the proposed scatter-and-gather query, illustrated in the following figure.
Models
nuScenes dataset
MethodBackboneEpochmAP1mAP2ConfigDownload
MapQRR502443.366.4configmodel
MapQRR5011050.572.6configmodel
Argoverse 2 dataset
MethodBackboneEpochdimmAP1mAP2ConfigDownload
MapQRR506244.868.1configmodel
MapQRR506341.265.4configmodel
  • mAP1 is measured under the thresholds { 0.2, 0.5, 1.0 }
  • mAP2 is measured under the thresholds { 0.5, 1.0, 1.5 }
Getting Started
These settings keep the same as MapTRv2
Note
If you meet nan during training, you could comment out this line:

MapQR/projects/mmdet3d_plugin/bevformer/modules/encoder.py

Line 149
in
d05554c
@auto_fp16() # This may cause 'grad_norm: nan'
Acknowledgements
MapQR is mainly based on MapTRv2.
It is also greatly inspired by the following outstanding contributions to the open-source community:
BEVFormer,
GKT,
ConditionalDETR,
DAB-DETR.
Citation
If you find MapQR is useful in your research or applications,
please consider giving us a star 🌟 and citing it by the following BibTeX entry.
@inproceedings{liu2024leveraging,
title={Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction},
author={Liu, Zihao and Zhang, Xiaoyu and Liu, Guangwei and Zhao, Ji and Xu, Ningyi},
booktitle={European Conference on Computer Vision},
year={2024}
}
About
[ECCV 2024] This is the official implementation of MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
arxiv.org/abs/2402.17430
Topics
autonomous-drivingbevdeep-learningdetreccv2024hd-map-constructiononline-hdmap-constructionself-driving-carvectorized-hdmap
Resources
Readme
MIT license
Activity
Custom properties
Stars
230 stars
Watchers
6 watching
Forks
21 forks
Report repository
Used by
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('^' + ".*" + '

Skip to content
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MapQR
Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction
Zihao Liu1*,
Xiaoyu Zhang2*,
Guangwei Liu3*,
Ji Zhao3#,
Ningyi Xu1#
1 Shanghai Jiao Tong University,
2 The Chinese University of Hong Kong,
3 Huixi Technology
*Equal contribution. #Corresponding author.
ArXiv Preprint (arXiv 2402.17430)
Accepted by ECCV 2024
🔥 News
  • 2024.08: 🎉🎉 Our new work HRMapNet is now released, it utilizes historical information to enhance HD map construction!
  • 2024.07: 🎉🎉 MapQR is accepted in ECCV 2024!
Overview
pipeline
This project introduces MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
Although the map construction is essentially a point set prediction task, MapQR utilizes instance queries rather than point queries.
These instance queries are scattered for the prediction of point sets and subsequently gathered for the final matching.
This query design, called the scatter-and-gather query, shares content information in the same map element and avoids possible inconsistency
of content information in point queries.
We further exploit prior information to enhance an instance query by adding positional information embedded from their reference points.
Together with a simple and effective improvement of a BEV encoder, the proposed MapQR achieves the best mean average precision (mAP) and maintains good efficiency on both nuScenes and Argoverse 2.
The main contribution is the proposed scatter-and-gather query, illustrated in the following figure.
Models
nuScenes dataset
MethodBackboneEpochmAP1mAP2ConfigDownload
MapQRR502443.366.4configmodel
MapQRR5011050.572.6configmodel
Argoverse 2 dataset
MethodBackboneEpochdimmAP1mAP2ConfigDownload
MapQRR506244.868.1configmodel
MapQRR506341.265.4configmodel
  • mAP1 is measured under the thresholds { 0.2, 0.5, 1.0 }
  • mAP2 is measured under the thresholds { 0.5, 1.0, 1.5 }
Getting Started
These settings keep the same as MapTRv2
Note
If you meet nan during training, you could comment out this line:

MapQR/projects/mmdet3d_plugin/bevformer/modules/encoder.py

Line 149
in
d05554c
@auto_fp16() # This may cause 'grad_norm: nan'
Acknowledgements
MapQR is mainly based on MapTRv2.
It is also greatly inspired by the following outstanding contributions to the open-source community:
BEVFormer,
GKT,
ConditionalDETR,
DAB-DETR.
Citation
If you find MapQR is useful in your research or applications,
please consider giving us a star 🌟 and citing it by the following BibTeX entry.
@inproceedings{liu2024leveraging,
title={Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction},
author={Liu, Zihao and Zhang, Xiaoyu and Liu, Guangwei and Zhao, Ji and Xu, Ningyi},
booktitle={European Conference on Computer Vision},
year={2024}
}
About
[ECCV 2024] This is the official implementation of MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
arxiv.org/abs/2402.17430
Topics
autonomous-drivingbevdeep-learningdetreccv2024hd-map-constructiononline-hdmap-constructionself-driving-carvectorized-hdmap
Resources
Readme
MIT license
Activity
Custom properties
Stars
230 stars
Watchers
6 watching
Forks
21 forks
Report repository
Used by
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
MapQR
Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction
Zihao Liu1*,
Xiaoyu Zhang2*,
Guangwei Liu3*,
Ji Zhao3#,
Ningyi Xu1#
1 Shanghai Jiao Tong University,
2 The Chinese University of Hong Kong,
3 Huixi Technology
*Equal contribution. #Corresponding author.
ArXiv Preprint (arXiv 2402.17430)
Accepted by ECCV 2024
🔥 News
  • 2024.08: 🎉🎉 Our new work HRMapNet is now released, it utilizes historical information to enhance HD map construction!
  • 2024.07: 🎉🎉 MapQR is accepted in ECCV 2024!
Overview
pipeline
This project introduces MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
Although the map construction is essentially a point set prediction task, MapQR utilizes instance queries rather than point queries.
These instance queries are scattered for the prediction of point sets and subsequently gathered for the final matching.
This query design, called the scatter-and-gather query, shares content information in the same map element and avoids possible inconsistency
of content information in point queries.
We further exploit prior information to enhance an instance query by adding positional information embedded from their reference points.
Together with a simple and effective improvement of a BEV encoder, the proposed MapQR achieves the best mean average precision (mAP) and maintains good efficiency on both nuScenes and Argoverse 2.
The main contribution is the proposed scatter-and-gather query, illustrated in the following figure.
Models
nuScenes dataset
MethodBackboneEpochmAP1mAP2ConfigDownload
MapQRR502443.366.4configmodel
MapQRR5011050.572.6configmodel
Argoverse 2 dataset
MethodBackboneEpochdimmAP1mAP2ConfigDownload
MapQRR506244.868.1configmodel
MapQRR506341.265.4configmodel
  • mAP1 is measured under the thresholds { 0.2, 0.5, 1.0 }
  • mAP2 is measured under the thresholds { 0.5, 1.0, 1.5 }
Getting Started
These settings keep the same as MapTRv2
Note
If you meet nan during training, you could comment out this line:

MapQR/projects/mmdet3d_plugin/bevformer/modules/encoder.py

Line 149
in
d05554c
@auto_fp16() # This may cause 'grad_norm: nan'
Acknowledgements
MapQR is mainly based on MapTRv2.
It is also greatly inspired by the following outstanding contributions to the open-source community:
BEVFormer,
GKT,
ConditionalDETR,
DAB-DETR.
Citation
If you find MapQR is useful in your research or applications,
please consider giving us a star 🌟 and citing it by the following BibTeX entry.
@inproceedings{liu2024leveraging,
title={Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction},
author={Liu, Zihao and Zhang, Xiaoyu and Liu, Guangwei and Zhao, Ji and Xu, Ningyi},
booktitle={European Conference on Computer Vision},
year={2024}
}
About
[ECCV 2024] This is the official implementation of MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
arxiv.org/abs/2402.17430
Topics
autonomous-drivingbevdeep-learningdetreccv2024hd-map-constructiononline-hdmap-constructionself-driving-carvectorized-hdmap
Resources
Readme
MIT license
Activity
Custom properties
Stars
230 stars
Watchers
6 watching
Forks
21 forks
Report repository
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
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})();
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MapQR
Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction
Zihao Liu1*,
Xiaoyu Zhang2*,
Guangwei Liu3*,
Ji Zhao3#,
Ningyi Xu1#
1 Shanghai Jiao Tong University,
2 The Chinese University of Hong Kong,
3 Huixi Technology
*Equal contribution. #Corresponding author.
ArXiv Preprint (arXiv 2402.17430)
Accepted by ECCV 2024
🔥 News
  • 2024.08: 🎉🎉 Our new work HRMapNet is now released, it utilizes historical information to enhance HD map construction!
  • 2024.07: 🎉🎉 MapQR is accepted in ECCV 2024!
Overview
pipeline
This project introduces MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
Although the map construction is essentially a point set prediction task, MapQR utilizes instance queries rather than point queries.
These instance queries are scattered for the prediction of point sets and subsequently gathered for the final matching.
This query design, called the scatter-and-gather query, shares content information in the same map element and avoids possible inconsistency
of content information in point queries.
We further exploit prior information to enhance an instance query by adding positional information embedded from their reference points.
Together with a simple and effective improvement of a BEV encoder, the proposed MapQR achieves the best mean average precision (mAP) and maintains good efficiency on both nuScenes and Argoverse 2.
The main contribution is the proposed scatter-and-gather query, illustrated in the following figure.
Models
nuScenes dataset
MethodBackboneEpochmAP1mAP2ConfigDownload
MapQRR502443.366.4configmodel
MapQRR5011050.572.6configmodel
Argoverse 2 dataset
MethodBackboneEpochdimmAP1mAP2ConfigDownload
MapQRR506244.868.1configmodel
MapQRR506341.265.4configmodel
  • mAP1 is measured under the thresholds { 0.2, 0.5, 1.0 }
  • mAP2 is measured under the thresholds { 0.5, 1.0, 1.5 }
Getting Started
These settings keep the same as MapTRv2
Note
If you meet nan during training, you could comment out this line:

MapQR/projects/mmdet3d_plugin/bevformer/modules/encoder.py

Line 149
in
d05554c
@auto_fp16() # This may cause 'grad_norm: nan'
Acknowledgements
MapQR is mainly based on MapTRv2.
It is also greatly inspired by the following outstanding contributions to the open-source community:
BEVFormer,
GKT,
ConditionalDETR,
DAB-DETR.
Citation
If you find MapQR is useful in your research or applications,
please consider giving us a star 🌟 and citing it by the following BibTeX entry.
@inproceedings{liu2024leveraging,
title={Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction},
author={Liu, Zihao and Zhang, Xiaoyu and Liu, Guangwei and Zhao, Ji and Xu, Ningyi},
booktitle={European Conference on Computer Vision},
year={2024}
}
About
[ECCV 2024] This is the official implementation of MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
arxiv.org/abs/2402.17430
Topics
autonomous-drivingbevdeep-learningdetreccv2024hd-map-constructiononline-hdmap-constructionself-driving-carvectorized-hdmap
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MIT license
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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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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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MapQR
Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction
Zihao Liu1*,
Xiaoyu Zhang2*,
Guangwei Liu3*,
Ji Zhao3#,
Ningyi Xu1#
1 Shanghai Jiao Tong University,
2 The Chinese University of Hong Kong,
3 Huixi Technology
*Equal contribution. #Corresponding author.
ArXiv Preprint (arXiv 2402.17430)
Accepted by ECCV 2024
🔥 News
  • 2024.08: 🎉🎉 Our new work HRMapNet is now released, it utilizes historical information to enhance HD map construction!
  • 2024.07: 🎉🎉 MapQR is accepted in ECCV 2024!
Overview
pipeline
This project introduces MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
Although the map construction is essentially a point set prediction task, MapQR utilizes instance queries rather than point queries.
These instance queries are scattered for the prediction of point sets and subsequently gathered for the final matching.
This query design, called the scatter-and-gather query, shares content information in the same map element and avoids possible inconsistency
of content information in point queries.
We further exploit prior information to enhance an instance query by adding positional information embedded from their reference points.
Together with a simple and effective improvement of a BEV encoder, the proposed MapQR achieves the best mean average precision (mAP) and maintains good efficiency on both nuScenes and Argoverse 2.
The main contribution is the proposed scatter-and-gather query, illustrated in the following figure.
Models
nuScenes dataset
MethodBackboneEpochmAP1mAP2ConfigDownload
MapQRR502443.366.4configmodel
MapQRR5011050.572.6configmodel
Argoverse 2 dataset
MethodBackboneEpochdimmAP1mAP2ConfigDownload
MapQRR506244.868.1configmodel
MapQRR506341.265.4configmodel
  • mAP1 is measured under the thresholds { 0.2, 0.5, 1.0 }
  • mAP2 is measured under the thresholds { 0.5, 1.0, 1.5 }
Getting Started
These settings keep the same as MapTRv2
Note
If you meet nan during training, you could comment out this line:

MapQR/projects/mmdet3d_plugin/bevformer/modules/encoder.py

Line 149
in
d05554c
@auto_fp16() # This may cause 'grad_norm: nan'
Acknowledgements
MapQR is mainly based on MapTRv2.
It is also greatly inspired by the following outstanding contributions to the open-source community:
BEVFormer,
GKT,
ConditionalDETR,
DAB-DETR.
Citation
If you find MapQR is useful in your research or applications,
please consider giving us a star 🌟 and citing it by the following BibTeX entry.
@inproceedings{liu2024leveraging,
title={Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction},
author={Liu, Zihao and Zhang, Xiaoyu and Liu, Guangwei and Zhao, Ji and Xu, Ningyi},
booktitle={European Conference on Computer Vision},
year={2024}
}
About
[ECCV 2024] This is the official implementation of MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
arxiv.org/abs/2402.17430
Topics
autonomous-drivingbevdeep-learningdetreccv2024hd-map-constructiononline-hdmap-constructionself-driving-carvectorized-hdmap
Resources
Readme
MIT license
Activity
Custom properties
Stars
230 stars
Watchers
6 watching
Forks
21 forks
Report repository
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
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MapQR
Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction
Zihao Liu1*,
Xiaoyu Zhang2*,
Guangwei Liu3*,
Ji Zhao3#,
Ningyi Xu1#
1 Shanghai Jiao Tong University,
2 The Chinese University of Hong Kong,
3 Huixi Technology
*Equal contribution. #Corresponding author.
ArXiv Preprint (arXiv 2402.17430)
Accepted by ECCV 2024
🔥 News
  • 2024.08: 🎉🎉 Our new work HRMapNet is now released, it utilizes historical information to enhance HD map construction!
  • 2024.07: 🎉🎉 MapQR is accepted in ECCV 2024!
Overview
pipeline
This project introduces MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
Although the map construction is essentially a point set prediction task, MapQR utilizes instance queries rather than point queries.
These instance queries are scattered for the prediction of point sets and subsequently gathered for the final matching.
This query design, called the scatter-and-gather query, shares content information in the same map element and avoids possible inconsistency
of content information in point queries.
We further exploit prior information to enhance an instance query by adding positional information embedded from their reference points.
Together with a simple and effective improvement of a BEV encoder, the proposed MapQR achieves the best mean average precision (mAP) and maintains good efficiency on both nuScenes and Argoverse 2.
The main contribution is the proposed scatter-and-gather query, illustrated in the following figure.
Models
nuScenes dataset
MethodBackboneEpochmAP1mAP2ConfigDownload
MapQRR502443.366.4configmodel
MapQRR5011050.572.6configmodel
Argoverse 2 dataset
MethodBackboneEpochdimmAP1mAP2ConfigDownload
MapQRR506244.868.1configmodel
MapQRR506341.265.4configmodel
  • mAP1 is measured under the thresholds { 0.2, 0.5, 1.0 }
  • mAP2 is measured under the thresholds { 0.5, 1.0, 1.5 }
Getting Started
These settings keep the same as MapTRv2
Note
If you meet nan during training, you could comment out this line:

MapQR/projects/mmdet3d_plugin/bevformer/modules/encoder.py

Line 149
in
d05554c
@auto_fp16() # This may cause 'grad_norm: nan'
Acknowledgements
MapQR is mainly based on MapTRv2.
It is also greatly inspired by the following outstanding contributions to the open-source community:
BEVFormer,
GKT,
ConditionalDETR,
DAB-DETR.
Citation
If you find MapQR is useful in your research or applications,
please consider giving us a star 🌟 and citing it by the following BibTeX entry.
@inproceedings{liu2024leveraging,
title={Leveraging Enhanced Queries of Point Sets for Vectorized Map Construction},
author={Liu, Zihao and Zhang, Xiaoyu and Liu, Guangwei and Zhao, Ji and Xu, Ningyi},
booktitle={European Conference on Computer Vision},
year={2024}
}
About
[ECCV 2024] This is the official implementation of MapQR, an end-to-end method with an emphasis on enhancing query capabilities for constructing online vectorized maps.
arxiv.org/abs/2402.17430
Topics
autonomous-drivingbevdeep-learningdetreccv2024hd-map-constructiononline-hdmap-constructionself-driving-carvectorized-hdmap
Resources
Readme
MIT license
Activity
Custom properties
Stars
230 stars
Watchers
6 watching
Forks
21 forks
Report repository
Used by
Contributors
Languages