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🔥How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation

🥳Accepted at ICML 2025🥳

IAL is a novel multi-modal framework for LiDAR-camera 3D panoptic segmentation. To address misalignment and weak fusion, IAL designs a synchronized augmentation (PieAug) and a geometric-guided fusion module (GTF) to align LiDAR and image features. Moreover, it leverages complementary priors from both modalities as queries through PQG for stronger instance prediction. On nuScenes, IAL achieves 82.3% PQ, and on SemanticKITTI, it obtains 63.1% PQ, significantly surpassing previous methods.

Demo

Framework Diagram

Framework Diagram

Preparation

Environments

Python == 3.8
CUDA == 11.1
pytorch == 1.10.1
mmcv == 2.0.0rc4
mmdet == 3.0.0
mmdet3d == 1.1.0
torch-scatter == 2.0.9
nms_lib

*Please see install.sh for more details.

Data Structure

Follow the mmdet3d to process the nuScenes dataset.

data/nuscenes_full/
├── gsam # save 2d proposals
├── lidarseg
├── maps
├── nuscenes_infos_test.pkl
├── nuscenes_infos_train.pkl
├── nuscenes_infos_val.pkl
├── panoptic
├── samples
├── sweeps
├── v1.0-trainval

You can generate *.pkl by excuting

python tools/create_data.py nuscenes --root-path data/nuscenes_full --out-dir data/nuscenes_full --extra-tag nuscenes

2D proposal Generation

# setup for Grounding-DINOcd tools
git clone https://github.com/IDEA-Research/GroundingDINO.git
cd GroundingDINO
pip install -e .# infer & save results
3-infer-gsam.sh

*Checkpoints and 2D preprocessing data are available huggingface and OneDrive.

Train & Inference

# train
sh 1-train.sh
# inference
sh 2-val.sh

Main Results

Framework Diagram

Visualization Results

Instance predictions on LiDAR and corresponding images. Error maps.

Citation

If you find this project helpful in your research, please consider citing our paper:

@inproceedings{pan2025ial,
title={How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation}, author={Yining Pan and Qiongjie Cui and Xulei Yang and Na Zhao},
booktitle = {Proceedings of the 42st International Conference on Machine Learning},
year={2025},
series = {ICML'25}
}

Acknowledgement

Many thanks to the following awesome open-source projects!

Closely Relevant Projects:

Other Awesome Projects:

About

[ICML2025] Official Code for IAL (Multi-modal 3D Panoptic Segmentation Model)

proceedings.mlr.press/v267/pan25c.html

Topics

3dsegmentationcomputer-visionicml-2025image-processinglidar-point-cloudmulti-modal-learningpanoptic-segmentation

Resources

Readme
Activity
Custom properties

Stars

32 stars

Watchers

2 watching

Forks

5 forks
Report repository

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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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🔥How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation
🥳Accepted at ICML 2025🥳
IAL is a novel multi-modal framework for LiDAR-camera 3D panoptic segmentation. To address misalignment and weak fusion, IAL designs a synchronized augmentation (PieAug) and a geometric-guided fusion module (GTF) to align LiDAR and image features. Moreover, it leverages complementary priors from both modalities as queries through PQG for stronger instance prediction. On nuScenes, IAL achieves 82.3% PQ, and on SemanticKITTI, it obtains 63.1% PQ, significantly surpassing previous methods.
Demo
Framework Diagram
Framework Diagram
Preparation
Environments
Python == 3.8 

CUDA == 11.1 

pytorch == 1.10.1 

mmcv == 2.0.0rc4 

mmdet == 3.0.0 

mmdet3d == 1.1.0 

torch-scatter == 2.0.9 
nms_lib
*Please see install.sh for more details.
Data Structure
Follow the mmdet3d to process the nuScenes dataset.
data/nuscenes_full/
├── gsam # save 2d proposals
├── lidarseg
├── maps
├── nuscenes_infos_test.pkl
├── nuscenes_infos_train.pkl
├── nuscenes_infos_val.pkl
├── panoptic
├── samples
├── sweeps
├── v1.0-trainval
You can generate *.pkl by excuting
python tools/create_data.py nuscenes --root-path data/nuscenes_full --out-dir data/nuscenes_full --extra-tag nuscenes
2D proposal Generation
# setup for Grounding-DINOcd tools
git clone https://github.com/IDEA-Research/GroundingDINO.git
cd GroundingDINO
pip install -e .# infer & save results
3-infer-gsam.sh
*Checkpoints and 2D preprocessing data are available huggingface and OneDrive.
Train & Inference
# train
sh 1-train.sh
# inference
sh 2-val.sh 
Main Results
Framework Diagram
Visualization Results
Instance predictions on LiDAR and corresponding images.

Error maps.

Citation
If you find this project helpful in your research, please consider citing our paper:
@inproceedings{pan2025ial,
title={How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation}, author={Yining Pan and Qiongjie Cui and Xulei Yang and Na Zhao},
booktitle = {Proceedings of the 42st International Conference on Machine Learning},
year={2025},
series = {ICML'25}
}
Acknowledgement
Many thanks to the following awesome open-source projects!
Closely Relevant Projects:
Other Awesome Projects:
About
[ICML2025] Official Code for IAL (Multi-modal 3D Panoptic Segmentation Model)
proceedings.mlr.press/v267/pan25c.html
Topics
3dsegmentationcomputer-visionicml-2025image-processinglidar-point-cloudmulti-modal-learningpanoptic-segmentation
Resources
Readme
Activity
Custom properties
Stars
32 stars
Watchers
2 watching
Forks
5 forks
Report repository
Releases
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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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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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🔥How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation
🥳Accepted at ICML 2025🥳
IAL is a novel multi-modal framework for LiDAR-camera 3D panoptic segmentation. To address misalignment and weak fusion, IAL designs a synchronized augmentation (PieAug) and a geometric-guided fusion module (GTF) to align LiDAR and image features. Moreover, it leverages complementary priors from both modalities as queries through PQG for stronger instance prediction. On nuScenes, IAL achieves 82.3% PQ, and on SemanticKITTI, it obtains 63.1% PQ, significantly surpassing previous methods.
Demo
Framework Diagram
Framework Diagram
Preparation
Environments
Python == 3.8 

CUDA == 11.1 

pytorch == 1.10.1 

mmcv == 2.0.0rc4 

mmdet == 3.0.0 

mmdet3d == 1.1.0 

torch-scatter == 2.0.9 
nms_lib
*Please see install.sh for more details.
Data Structure
Follow the mmdet3d to process the nuScenes dataset.
data/nuscenes_full/
├── gsam # save 2d proposals
├── lidarseg
├── maps
├── nuscenes_infos_test.pkl
├── nuscenes_infos_train.pkl
├── nuscenes_infos_val.pkl
├── panoptic
├── samples
├── sweeps
├── v1.0-trainval
You can generate *.pkl by excuting
python tools/create_data.py nuscenes --root-path data/nuscenes_full --out-dir data/nuscenes_full --extra-tag nuscenes
2D proposal Generation
# setup for Grounding-DINOcd tools
git clone https://github.com/IDEA-Research/GroundingDINO.git
cd GroundingDINO
pip install -e .# infer & save results
3-infer-gsam.sh
*Checkpoints and 2D preprocessing data are available huggingface and OneDrive.
Train & Inference
# train
sh 1-train.sh
# inference
sh 2-val.sh 
Main Results
Framework Diagram
Visualization Results
Instance predictions on LiDAR and corresponding images.

Error maps.

Citation
If you find this project helpful in your research, please consider citing our paper:
@inproceedings{pan2025ial,
title={How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation}, author={Yining Pan and Qiongjie Cui and Xulei Yang and Na Zhao},
booktitle = {Proceedings of the 42st International Conference on Machine Learning},
year={2025},
series = {ICML'25}
}
Acknowledgement
Many thanks to the following awesome open-source projects!
Closely Relevant Projects:
Other Awesome Projects:
About
[ICML2025] Official Code for IAL (Multi-modal 3D Panoptic Segmentation Model)
proceedings.mlr.press/v267/pan25c.html
Topics
3dsegmentationcomputer-visionicml-2025image-processinglidar-point-cloudmulti-modal-learningpanoptic-segmentation
Resources
Readme
Activity
Custom properties
Stars
32 stars
Watchers
2 watching
Forks
5 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('^' + ".*" + '

Skip to content
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🔥How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation
🥳Accepted at ICML 2025🥳
IAL is a novel multi-modal framework for LiDAR-camera 3D panoptic segmentation. To address misalignment and weak fusion, IAL designs a synchronized augmentation (PieAug) and a geometric-guided fusion module (GTF) to align LiDAR and image features. Moreover, it leverages complementary priors from both modalities as queries through PQG for stronger instance prediction. On nuScenes, IAL achieves 82.3% PQ, and on SemanticKITTI, it obtains 63.1% PQ, significantly surpassing previous methods.
Demo
Framework Diagram
Framework Diagram
Preparation
Environments
Python == 3.8 

CUDA == 11.1 

pytorch == 1.10.1 

mmcv == 2.0.0rc4 

mmdet == 3.0.0 

mmdet3d == 1.1.0 

torch-scatter == 2.0.9 
nms_lib
*Please see install.sh for more details.
Data Structure
Follow the mmdet3d to process the nuScenes dataset.
data/nuscenes_full/
├── gsam # save 2d proposals
├── lidarseg
├── maps
├── nuscenes_infos_test.pkl
├── nuscenes_infos_train.pkl
├── nuscenes_infos_val.pkl
├── panoptic
├── samples
├── sweeps
├── v1.0-trainval
You can generate *.pkl by excuting
python tools/create_data.py nuscenes --root-path data/nuscenes_full --out-dir data/nuscenes_full --extra-tag nuscenes
2D proposal Generation
# setup for Grounding-DINOcd tools
git clone https://github.com/IDEA-Research/GroundingDINO.git
cd GroundingDINO
pip install -e .# infer & save results
3-infer-gsam.sh
*Checkpoints and 2D preprocessing data are available huggingface and OneDrive.
Train & Inference
# train
sh 1-train.sh
# inference
sh 2-val.sh 
Main Results
Framework Diagram
Visualization Results
Instance predictions on LiDAR and corresponding images.

Error maps.

Citation
If you find this project helpful in your research, please consider citing our paper:
@inproceedings{pan2025ial,
title={How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation}, author={Yining Pan and Qiongjie Cui and Xulei Yang and Na Zhao},
booktitle = {Proceedings of the 42st International Conference on Machine Learning},
year={2025},
series = {ICML'25}
}
Acknowledgement
Many thanks to the following awesome open-source projects!
Closely Relevant Projects:
Other Awesome Projects:
About
[ICML2025] Official Code for IAL (Multi-modal 3D Panoptic Segmentation Model)
proceedings.mlr.press/v267/pan25c.html
Topics
3dsegmentationcomputer-visionicml-2025image-processinglidar-point-cloudmulti-modal-learningpanoptic-segmentation
Resources
Readme
Activity
Custom properties
Stars
32 stars
Watchers
2 watching
Forks
5 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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🔥How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation
🥳Accepted at ICML 2025🥳
IAL is a novel multi-modal framework for LiDAR-camera 3D panoptic segmentation. To address misalignment and weak fusion, IAL designs a synchronized augmentation (PieAug) and a geometric-guided fusion module (GTF) to align LiDAR and image features. Moreover, it leverages complementary priors from both modalities as queries through PQG for stronger instance prediction. On nuScenes, IAL achieves 82.3% PQ, and on SemanticKITTI, it obtains 63.1% PQ, significantly surpassing previous methods.
Demo
Framework Diagram
Framework Diagram
Preparation
Environments
Python == 3.8 

CUDA == 11.1 

pytorch == 1.10.1 

mmcv == 2.0.0rc4 

mmdet == 3.0.0 

mmdet3d == 1.1.0 

torch-scatter == 2.0.9 
nms_lib
*Please see install.sh for more details.
Data Structure
Follow the mmdet3d to process the nuScenes dataset.
data/nuscenes_full/
├── gsam # save 2d proposals
├── lidarseg
├── maps
├── nuscenes_infos_test.pkl
├── nuscenes_infos_train.pkl
├── nuscenes_infos_val.pkl
├── panoptic
├── samples
├── sweeps
├── v1.0-trainval
You can generate *.pkl by excuting
python tools/create_data.py nuscenes --root-path data/nuscenes_full --out-dir data/nuscenes_full --extra-tag nuscenes
2D proposal Generation
# setup for Grounding-DINOcd tools
git clone https://github.com/IDEA-Research/GroundingDINO.git
cd GroundingDINO
pip install -e .# infer & save results
3-infer-gsam.sh
*Checkpoints and 2D preprocessing data are available huggingface and OneDrive.
Train & Inference
# train
sh 1-train.sh
# inference
sh 2-val.sh 
Main Results
Framework Diagram
Visualization Results
Instance predictions on LiDAR and corresponding images.

Error maps.

Citation
If you find this project helpful in your research, please consider citing our paper:
@inproceedings{pan2025ial,
title={How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation}, author={Yining Pan and Qiongjie Cui and Xulei Yang and Na Zhao},
booktitle = {Proceedings of the 42st International Conference on Machine Learning},
year={2025},
series = {ICML'25}
}
Acknowledgement
Many thanks to the following awesome open-source projects!
Closely Relevant Projects:
Other Awesome Projects:
About
[ICML2025] Official Code for IAL (Multi-modal 3D Panoptic Segmentation Model)
proceedings.mlr.press/v267/pan25c.html
Topics
3dsegmentationcomputer-visionicml-2025image-processinglidar-point-cloudmulti-modal-learningpanoptic-segmentation
Resources
Readme
Activity
Custom properties
Stars
32 stars
Watchers
2 watching
Forks
5 forks
Report repository
Releases
Packages
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); }
})();
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var __m = "*";
var __re = new RegExp('^' + ".*" + '

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🔥How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation
🥳Accepted at ICML 2025🥳
IAL is a novel multi-modal framework for LiDAR-camera 3D panoptic segmentation. To address misalignment and weak fusion, IAL designs a synchronized augmentation (PieAug) and a geometric-guided fusion module (GTF) to align LiDAR and image features. Moreover, it leverages complementary priors from both modalities as queries through PQG for stronger instance prediction. On nuScenes, IAL achieves 82.3% PQ, and on SemanticKITTI, it obtains 63.1% PQ, significantly surpassing previous methods.
Demo
Framework Diagram
Framework Diagram
Preparation
Environments
Python == 3.8 

CUDA == 11.1 

pytorch == 1.10.1 

mmcv == 2.0.0rc4 

mmdet == 3.0.0 

mmdet3d == 1.1.0 

torch-scatter == 2.0.9 
nms_lib
*Please see install.sh for more details.
Data Structure
Follow the mmdet3d to process the nuScenes dataset.
data/nuscenes_full/
├── gsam # save 2d proposals
├── lidarseg
├── maps
├── nuscenes_infos_test.pkl
├── nuscenes_infos_train.pkl
├── nuscenes_infos_val.pkl
├── panoptic
├── samples
├── sweeps
├── v1.0-trainval
You can generate *.pkl by excuting
python tools/create_data.py nuscenes --root-path data/nuscenes_full --out-dir data/nuscenes_full --extra-tag nuscenes
2D proposal Generation
# setup for Grounding-DINOcd tools
git clone https://github.com/IDEA-Research/GroundingDINO.git
cd GroundingDINO
pip install -e .# infer & save results
3-infer-gsam.sh
*Checkpoints and 2D preprocessing data are available huggingface and OneDrive.
Train & Inference
# train
sh 1-train.sh
# inference
sh 2-val.sh 
Main Results
Framework Diagram
Visualization Results
Instance predictions on LiDAR and corresponding images.

Error maps.

Citation
If you find this project helpful in your research, please consider citing our paper:
@inproceedings{pan2025ial,
title={How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation}, author={Yining Pan and Qiongjie Cui and Xulei Yang and Na Zhao},
booktitle = {Proceedings of the 42st International Conference on Machine Learning},
year={2025},
series = {ICML'25}
}
Acknowledgement
Many thanks to the following awesome open-source projects!
Closely Relevant Projects:
Other Awesome Projects:
About
[ICML2025] Official Code for IAL (Multi-modal 3D Panoptic Segmentation Model)
proceedings.mlr.press/v267/pan25c.html
Topics
3dsegmentationcomputer-visionicml-2025image-processinglidar-point-cloudmulti-modal-learningpanoptic-segmentation
Resources
Readme
Activity
Custom properties
Stars
32 stars
Watchers
2 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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🔥How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation
🥳Accepted at ICML 2025🥳
IAL is a novel multi-modal framework for LiDAR-camera 3D panoptic segmentation. To address misalignment and weak fusion, IAL designs a synchronized augmentation (PieAug) and a geometric-guided fusion module (GTF) to align LiDAR and image features. Moreover, it leverages complementary priors from both modalities as queries through PQG for stronger instance prediction. On nuScenes, IAL achieves 82.3% PQ, and on SemanticKITTI, it obtains 63.1% PQ, significantly surpassing previous methods.
Demo
Framework Diagram
Framework Diagram
Preparation
Environments
Python == 3.8 

CUDA == 11.1 

pytorch == 1.10.1 

mmcv == 2.0.0rc4 

mmdet == 3.0.0 

mmdet3d == 1.1.0 

torch-scatter == 2.0.9 
nms_lib
*Please see install.sh for more details.
Data Structure
Follow the mmdet3d to process the nuScenes dataset.
data/nuscenes_full/
├── gsam # save 2d proposals
├── lidarseg
├── maps
├── nuscenes_infos_test.pkl
├── nuscenes_infos_train.pkl
├── nuscenes_infos_val.pkl
├── panoptic
├── samples
├── sweeps
├── v1.0-trainval
You can generate *.pkl by excuting
python tools/create_data.py nuscenes --root-path data/nuscenes_full --out-dir data/nuscenes_full --extra-tag nuscenes
2D proposal Generation
# setup for Grounding-DINOcd tools
git clone https://github.com/IDEA-Research/GroundingDINO.git
cd GroundingDINO
pip install -e .# infer & save results
3-infer-gsam.sh
*Checkpoints and 2D preprocessing data are available huggingface and OneDrive.
Train & Inference
# train
sh 1-train.sh
# inference
sh 2-val.sh 
Main Results
Framework Diagram
Visualization Results
Instance predictions on LiDAR and corresponding images.

Error maps.

Citation
If you find this project helpful in your research, please consider citing our paper:
@inproceedings{pan2025ial,
title={How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation}, author={Yining Pan and Qiongjie Cui and Xulei Yang and Na Zhao},
booktitle = {Proceedings of the 42st International Conference on Machine Learning},
year={2025},
series = {ICML'25}
}
Acknowledgement
Many thanks to the following awesome open-source projects!
Closely Relevant Projects:
Other Awesome Projects:
About
[ICML2025] Official Code for IAL (Multi-modal 3D Panoptic Segmentation Model)
proceedings.mlr.press/v267/pan25c.html
Topics
3dsegmentationcomputer-visionicml-2025image-processinglidar-point-cloudmulti-modal-learningpanoptic-segmentation
Resources
Readme
Activity
Custom properties
Stars
32 stars
Watchers
2 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
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Skip to content
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🔥How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation
🥳Accepted at ICML 2025🥳
IAL is a novel multi-modal framework for LiDAR-camera 3D panoptic segmentation. To address misalignment and weak fusion, IAL designs a synchronized augmentation (PieAug) and a geometric-guided fusion module (GTF) to align LiDAR and image features. Moreover, it leverages complementary priors from both modalities as queries through PQG for stronger instance prediction. On nuScenes, IAL achieves 82.3% PQ, and on SemanticKITTI, it obtains 63.1% PQ, significantly surpassing previous methods.
Demo
Framework Diagram
Framework Diagram
Preparation
Environments
Python == 3.8 

CUDA == 11.1 

pytorch == 1.10.1 

mmcv == 2.0.0rc4 

mmdet == 3.0.0 

mmdet3d == 1.1.0 

torch-scatter == 2.0.9 
nms_lib
*Please see install.sh for more details.
Data Structure
Follow the mmdet3d to process the nuScenes dataset.
data/nuscenes_full/
├── gsam # save 2d proposals
├── lidarseg
├── maps
├── nuscenes_infos_test.pkl
├── nuscenes_infos_train.pkl
├── nuscenes_infos_val.pkl
├── panoptic
├── samples
├── sweeps
├── v1.0-trainval
You can generate *.pkl by excuting
python tools/create_data.py nuscenes --root-path data/nuscenes_full --out-dir data/nuscenes_full --extra-tag nuscenes
2D proposal Generation
# setup for Grounding-DINOcd tools
git clone https://github.com/IDEA-Research/GroundingDINO.git
cd GroundingDINO
pip install -e .# infer & save results
3-infer-gsam.sh
*Checkpoints and 2D preprocessing data are available huggingface and OneDrive.
Train & Inference
# train
sh 1-train.sh
# inference
sh 2-val.sh 
Main Results
Framework Diagram
Visualization Results
Instance predictions on LiDAR and corresponding images.

Error maps.

Citation
If you find this project helpful in your research, please consider citing our paper:
@inproceedings{pan2025ial,
title={How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation}, author={Yining Pan and Qiongjie Cui and Xulei Yang and Na Zhao},
booktitle = {Proceedings of the 42st International Conference on Machine Learning},
year={2025},
series = {ICML'25}
}
Acknowledgement
Many thanks to the following awesome open-source projects!
Closely Relevant Projects:
Other Awesome Projects:
About
[ICML2025] Official Code for IAL (Multi-modal 3D Panoptic Segmentation Model)
proceedings.mlr.press/v267/pan25c.html
Topics
3dsegmentationcomputer-visionicml-2025image-processinglidar-point-cloudmulti-modal-learningpanoptic-segmentation
Resources
Readme
Activity
Custom properties
Stars
32 stars
Watchers
2 watching
Forks
5 forks
Report repository
Releases
Packages
Contributors
Languages