Skip to content
BranchesTags

Repository files navigation

The official implementation of "Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images" to be presented in IROS 2024.

Note

  • Thanks for you attention and interest!

Setup

  • Config Setting : edit ./config/config.json such that "dataset_dir" correctly locates the directory where your nuScenes dataset is stored. In addition, add your targets (e.g., 'vehicle', 'road', 'lane', 'pedestrian') into "target" in ./config/Scratch/data.json. For example, if you set "target":["vehicle", "pedestrian"], the model will automatically be configured and trained to predict both 'vehicle' and 'pedestrian'.

  • Implemenation Environment : The model is implemented by using Pytorch. We share our anaconda environment in the folder 'anaconda_env'. We trained our model on a server equipped with 4 NVIDIA GeForce RTX 4090 graphic cards. To run the deformable attention of BEVFormer, you need to install CUDA first (the version 11.1 is installed in our server) and then compile dedicated CUDA operators in ./models/ops as follows.

$ cd ./models/ops
$ sh ./make.sh
$ python test.py # unit test (should see all checking is True)

Train and Test New Models

To train the model from scratch, run the followings. The network parameters of the trained models will be stored in the folder saved_models.

$ sh run_train.sh

argumentparser.py have a number of command-line flags that you can use to configure the model architecture, hyperparameters, and input / output settings. You can find the descriptions in the file.

To test the trained model, first edit the parameter 'exp_id' in 'run_test.sh' file to match your experiment id and run the followings. You also need to set 'target' in the file to one of four categories (vehicle, pedestrian, road, lane)

$ sh run_test.sh

Test Pre-trained Models

To test the pre-trained models, first download the pre-trained model parameters from Here. Next, copy them into 'saved_models' folder. Finally, edit the parameter 'exp_id' in 'run_test.sh' file to match the downloaded experiment id and run the followings. It is worth nothing that we trained our models four times each of which corresponds to one of the four classes.

$ python run_test.sh

Paper Download

Arxiv

Citation

@InProceedings{Choi,
author = {D. Choi and J. Kang and T. An and K. An and K. Min},
title = {Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images},
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2024}
}

Acknowledgement

We would like to thank the authors of CVT, BEVFormer, FIERY, LSS for their sharing the original implementation codes. Our work is highly inspired and motivated not only by the mentioned works but also by other predecessors.

About

The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024.

Topics

autonomous-drivingbevsemantic-segmentationsurrounding-images

Resources

Readme
MIT license
Activity

Stars

2 stars

Watchers

1 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
GitHub - d1024choi/ProgressiveQueryRefineNet: The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024. · GitHub
Skip to content
BranchesTags
Repository files navigation
The official implementation of "Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images" to be presented in IROS 2024.
Note
  • Thanks for you attention and interest!
Setup
  • Config Setting : edit ./config/config.json such that "dataset_dir" correctly locates the directory where your nuScenes dataset is stored. In addition, add your targets (e.g., 'vehicle', 'road', 'lane', 'pedestrian') into "target" in ./config/Scratch/data.json. For example, if you set "target":["vehicle", "pedestrian"], the model will automatically be configured and trained to predict both 'vehicle' and 'pedestrian'.
  • Implemenation Environment : The model is implemented by using Pytorch. We share our anaconda environment in the folder 'anaconda_env'. We trained our model on a server equipped with 4 NVIDIA GeForce RTX 4090 graphic cards. To run the deformable attention of BEVFormer, you need to install CUDA first (the version 11.1 is installed in our server) and then compile dedicated CUDA operators in ./models/ops as follows.
$ cd ./models/ops
$ sh ./make.sh
$ python test.py # unit test (should see all checking is True)
Train and Test New Models
To train the model from scratch, run the followings. The network parameters of the trained models will be stored in the folder saved_models.
$ sh run_train.sh
argumentparser.py have a number of command-line flags that you can use to configure the model architecture, hyperparameters, and input / output settings. You can find the descriptions in the file.
To test the trained model, first edit the parameter 'exp_id' in 'run_test.sh' file to match your experiment id and run the followings. You also need to set 'target' in the file to one of four categories (vehicle, pedestrian, road, lane)
$ sh run_test.sh
Test Pre-trained Models
To test the pre-trained models, first download the pre-trained model parameters from Here. Next, copy them into 'saved_models' folder. Finally, edit the parameter 'exp_id' in 'run_test.sh' file to match the downloaded experiment id and run the followings. It is worth nothing that we trained our models four times each of which corresponds to one of the four classes.
$ python run_test.sh
Paper Download
Arxiv
Citation
@InProceedings{Choi,
author = {D. Choi and J. Kang and T. An and K. An and K. Min},
title = {Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images},
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2024}
}

Acknowledgement
We would like to thank the authors of CVT, BEVFormer, FIERY, LSS for their sharing the original implementation codes. Our work is highly inspired and motivated not only by the mentioned works but also by other predecessors.
About
The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024.
Topics
autonomous-drivingbevsemantic-segmentationsurrounding-images
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - d1024choi/ProgressiveQueryRefineNet: The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024. · GitHub
Skip to content
BranchesTags
Repository files navigation
The official implementation of "Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images" to be presented in IROS 2024.
Note
  • Thanks for you attention and interest!
Setup
  • Config Setting : edit ./config/config.json such that "dataset_dir" correctly locates the directory where your nuScenes dataset is stored. In addition, add your targets (e.g., 'vehicle', 'road', 'lane', 'pedestrian') into "target" in ./config/Scratch/data.json. For example, if you set "target":["vehicle", "pedestrian"], the model will automatically be configured and trained to predict both 'vehicle' and 'pedestrian'.
  • Implemenation Environment : The model is implemented by using Pytorch. We share our anaconda environment in the folder 'anaconda_env'. We trained our model on a server equipped with 4 NVIDIA GeForce RTX 4090 graphic cards. To run the deformable attention of BEVFormer, you need to install CUDA first (the version 11.1 is installed in our server) and then compile dedicated CUDA operators in ./models/ops as follows.
$ cd ./models/ops
$ sh ./make.sh
$ python test.py # unit test (should see all checking is True)
Train and Test New Models
To train the model from scratch, run the followings. The network parameters of the trained models will be stored in the folder saved_models.
$ sh run_train.sh
argumentparser.py have a number of command-line flags that you can use to configure the model architecture, hyperparameters, and input / output settings. You can find the descriptions in the file.
To test the trained model, first edit the parameter 'exp_id' in 'run_test.sh' file to match your experiment id and run the followings. You also need to set 'target' in the file to one of four categories (vehicle, pedestrian, road, lane)
$ sh run_test.sh
Test Pre-trained Models
To test the pre-trained models, first download the pre-trained model parameters from Here. Next, copy them into 'saved_models' folder. Finally, edit the parameter 'exp_id' in 'run_test.sh' file to match the downloaded experiment id and run the followings. It is worth nothing that we trained our models four times each of which corresponds to one of the four classes.
$ python run_test.sh
Paper Download
Arxiv
Citation
@InProceedings{Choi,
author = {D. Choi and J. Kang and T. An and K. An and K. Min},
title = {Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images},
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2024}
}

Acknowledgement
We would like to thank the authors of CVT, BEVFormer, FIERY, LSS for their sharing the original implementation codes. Our work is highly inspired and motivated not only by the mentioned works but also by other predecessors.
About
The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024.
Topics
autonomous-drivingbevsemantic-segmentationsurrounding-images
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - d1024choi/ProgressiveQueryRefineNet: The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024. · GitHub
Skip to content
BranchesTags
Repository files navigation
The official implementation of "Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images" to be presented in IROS 2024.
Note
  • Thanks for you attention and interest!
Setup
  • Config Setting : edit ./config/config.json such that "dataset_dir" correctly locates the directory where your nuScenes dataset is stored. In addition, add your targets (e.g., 'vehicle', 'road', 'lane', 'pedestrian') into "target" in ./config/Scratch/data.json. For example, if you set "target":["vehicle", "pedestrian"], the model will automatically be configured and trained to predict both 'vehicle' and 'pedestrian'.
  • Implemenation Environment : The model is implemented by using Pytorch. We share our anaconda environment in the folder 'anaconda_env'. We trained our model on a server equipped with 4 NVIDIA GeForce RTX 4090 graphic cards. To run the deformable attention of BEVFormer, you need to install CUDA first (the version 11.1 is installed in our server) and then compile dedicated CUDA operators in ./models/ops as follows.
$ cd ./models/ops
$ sh ./make.sh
$ python test.py # unit test (should see all checking is True)
Train and Test New Models
To train the model from scratch, run the followings. The network parameters of the trained models will be stored in the folder saved_models.
$ sh run_train.sh
argumentparser.py have a number of command-line flags that you can use to configure the model architecture, hyperparameters, and input / output settings. You can find the descriptions in the file.
To test the trained model, first edit the parameter 'exp_id' in 'run_test.sh' file to match your experiment id and run the followings. You also need to set 'target' in the file to one of four categories (vehicle, pedestrian, road, lane)
$ sh run_test.sh
Test Pre-trained Models
To test the pre-trained models, first download the pre-trained model parameters from Here. Next, copy them into 'saved_models' folder. Finally, edit the parameter 'exp_id' in 'run_test.sh' file to match the downloaded experiment id and run the followings. It is worth nothing that we trained our models four times each of which corresponds to one of the four classes.
$ python run_test.sh
Paper Download
Arxiv
Citation
@InProceedings{Choi,
author = {D. Choi and J. Kang and T. An and K. An and K. Min},
title = {Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images},
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2024}
}

Acknowledgement
We would like to thank the authors of CVT, BEVFormer, FIERY, LSS for their sharing the original implementation codes. Our work is highly inspired and motivated not only by the mentioned works but also by other predecessors.
About
The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024.
Topics
autonomous-drivingbevsemantic-segmentationsurrounding-images
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - d1024choi/ProgressiveQueryRefineNet: The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024. · GitHub
Skip to content
BranchesTags
Repository files navigation
The official implementation of "Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images" to be presented in IROS 2024.
Note
  • Thanks for you attention and interest!
Setup
  • Config Setting : edit ./config/config.json such that "dataset_dir" correctly locates the directory where your nuScenes dataset is stored. In addition, add your targets (e.g., 'vehicle', 'road', 'lane', 'pedestrian') into "target" in ./config/Scratch/data.json. For example, if you set "target":["vehicle", "pedestrian"], the model will automatically be configured and trained to predict both 'vehicle' and 'pedestrian'.
  • Implemenation Environment : The model is implemented by using Pytorch. We share our anaconda environment in the folder 'anaconda_env'. We trained our model on a server equipped with 4 NVIDIA GeForce RTX 4090 graphic cards. To run the deformable attention of BEVFormer, you need to install CUDA first (the version 11.1 is installed in our server) and then compile dedicated CUDA operators in ./models/ops as follows.
$ cd ./models/ops
$ sh ./make.sh
$ python test.py # unit test (should see all checking is True)
Train and Test New Models
To train the model from scratch, run the followings. The network parameters of the trained models will be stored in the folder saved_models.
$ sh run_train.sh
argumentparser.py have a number of command-line flags that you can use to configure the model architecture, hyperparameters, and input / output settings. You can find the descriptions in the file.
To test the trained model, first edit the parameter 'exp_id' in 'run_test.sh' file to match your experiment id and run the followings. You also need to set 'target' in the file to one of four categories (vehicle, pedestrian, road, lane)
$ sh run_test.sh
Test Pre-trained Models
To test the pre-trained models, first download the pre-trained model parameters from Here. Next, copy them into 'saved_models' folder. Finally, edit the parameter 'exp_id' in 'run_test.sh' file to match the downloaded experiment id and run the followings. It is worth nothing that we trained our models four times each of which corresponds to one of the four classes.
$ python run_test.sh
Paper Download
Arxiv
Citation
@InProceedings{Choi,
author = {D. Choi and J. Kang and T. An and K. An and K. Min},
title = {Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images},
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2024}
}

Acknowledgement
We would like to thank the authors of CVT, BEVFormer, FIERY, LSS for their sharing the original implementation codes. Our work is highly inspired and motivated not only by the mentioned works but also by other predecessors.
About
The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024.
Topics
autonomous-drivingbevsemantic-segmentationsurrounding-images
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - d1024choi/ProgressiveQueryRefineNet: The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024. · GitHub
Skip to content
BranchesTags
Repository files navigation
The official implementation of "Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images" to be presented in IROS 2024.
Note
  • Thanks for you attention and interest!
Setup
  • Config Setting : edit ./config/config.json such that "dataset_dir" correctly locates the directory where your nuScenes dataset is stored. In addition, add your targets (e.g., 'vehicle', 'road', 'lane', 'pedestrian') into "target" in ./config/Scratch/data.json. For example, if you set "target":["vehicle", "pedestrian"], the model will automatically be configured and trained to predict both 'vehicle' and 'pedestrian'.
  • Implemenation Environment : The model is implemented by using Pytorch. We share our anaconda environment in the folder 'anaconda_env'. We trained our model on a server equipped with 4 NVIDIA GeForce RTX 4090 graphic cards. To run the deformable attention of BEVFormer, you need to install CUDA first (the version 11.1 is installed in our server) and then compile dedicated CUDA operators in ./models/ops as follows.
$ cd ./models/ops
$ sh ./make.sh
$ python test.py # unit test (should see all checking is True)
Train and Test New Models
To train the model from scratch, run the followings. The network parameters of the trained models will be stored in the folder saved_models.
$ sh run_train.sh
argumentparser.py have a number of command-line flags that you can use to configure the model architecture, hyperparameters, and input / output settings. You can find the descriptions in the file.
To test the trained model, first edit the parameter 'exp_id' in 'run_test.sh' file to match your experiment id and run the followings. You also need to set 'target' in the file to one of four categories (vehicle, pedestrian, road, lane)
$ sh run_test.sh
Test Pre-trained Models
To test the pre-trained models, first download the pre-trained model parameters from Here. Next, copy them into 'saved_models' folder. Finally, edit the parameter 'exp_id' in 'run_test.sh' file to match the downloaded experiment id and run the followings. It is worth nothing that we trained our models four times each of which corresponds to one of the four classes.
$ python run_test.sh
Paper Download
Arxiv
Citation
@InProceedings{Choi,
author = {D. Choi and J. Kang and T. An and K. An and K. Min},
title = {Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images},
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2024}
}

Acknowledgement
We would like to thank the authors of CVT, BEVFormer, FIERY, LSS for their sharing the original implementation codes. Our work is highly inspired and motivated not only by the mentioned works but also by other predecessors.
About
The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024.
Topics
autonomous-drivingbevsemantic-segmentationsurrounding-images
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - d1024choi/ProgressiveQueryRefineNet: The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024. · GitHub
Skip to content
BranchesTags
Repository files navigation
The official implementation of "Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images" to be presented in IROS 2024.
Note
  • Thanks for you attention and interest!
Setup
  • Config Setting : edit ./config/config.json such that "dataset_dir" correctly locates the directory where your nuScenes dataset is stored. In addition, add your targets (e.g., 'vehicle', 'road', 'lane', 'pedestrian') into "target" in ./config/Scratch/data.json. For example, if you set "target":["vehicle", "pedestrian"], the model will automatically be configured and trained to predict both 'vehicle' and 'pedestrian'.
  • Implemenation Environment : The model is implemented by using Pytorch. We share our anaconda environment in the folder 'anaconda_env'. We trained our model on a server equipped with 4 NVIDIA GeForce RTX 4090 graphic cards. To run the deformable attention of BEVFormer, you need to install CUDA first (the version 11.1 is installed in our server) and then compile dedicated CUDA operators in ./models/ops as follows.
$ cd ./models/ops
$ sh ./make.sh
$ python test.py # unit test (should see all checking is True)
Train and Test New Models
To train the model from scratch, run the followings. The network parameters of the trained models will be stored in the folder saved_models.
$ sh run_train.sh
argumentparser.py have a number of command-line flags that you can use to configure the model architecture, hyperparameters, and input / output settings. You can find the descriptions in the file.
To test the trained model, first edit the parameter 'exp_id' in 'run_test.sh' file to match your experiment id and run the followings. You also need to set 'target' in the file to one of four categories (vehicle, pedestrian, road, lane)
$ sh run_test.sh
Test Pre-trained Models
To test the pre-trained models, first download the pre-trained model parameters from Here. Next, copy them into 'saved_models' folder. Finally, edit the parameter 'exp_id' in 'run_test.sh' file to match the downloaded experiment id and run the followings. It is worth nothing that we trained our models four times each of which corresponds to one of the four classes.
$ python run_test.sh
Paper Download
Arxiv
Citation
@InProceedings{Choi,
author = {D. Choi and J. Kang and T. An and K. An and K. Min},
title = {Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images},
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2024}
}

Acknowledgement
We would like to thank the authors of CVT, BEVFormer, FIERY, LSS for their sharing the original implementation codes. Our work is highly inspired and motivated not only by the mentioned works but also by other predecessors.
About
The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024.
Topics
autonomous-drivingbevsemantic-segmentationsurrounding-images
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - d1024choi/ProgressiveQueryRefineNet: The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024. · GitHub
Skip to content
BranchesTags
Repository files navigation
The official implementation of "Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images" to be presented in IROS 2024.
Note
  • Thanks for you attention and interest!
Setup
  • Config Setting : edit ./config/config.json such that "dataset_dir" correctly locates the directory where your nuScenes dataset is stored. In addition, add your targets (e.g., 'vehicle', 'road', 'lane', 'pedestrian') into "target" in ./config/Scratch/data.json. For example, if you set "target":["vehicle", "pedestrian"], the model will automatically be configured and trained to predict both 'vehicle' and 'pedestrian'.
  • Implemenation Environment : The model is implemented by using Pytorch. We share our anaconda environment in the folder 'anaconda_env'. We trained our model on a server equipped with 4 NVIDIA GeForce RTX 4090 graphic cards. To run the deformable attention of BEVFormer, you need to install CUDA first (the version 11.1 is installed in our server) and then compile dedicated CUDA operators in ./models/ops as follows.
$ cd ./models/ops
$ sh ./make.sh
$ python test.py # unit test (should see all checking is True)
Train and Test New Models
To train the model from scratch, run the followings. The network parameters of the trained models will be stored in the folder saved_models.
$ sh run_train.sh
argumentparser.py have a number of command-line flags that you can use to configure the model architecture, hyperparameters, and input / output settings. You can find the descriptions in the file.
To test the trained model, first edit the parameter 'exp_id' in 'run_test.sh' file to match your experiment id and run the followings. You also need to set 'target' in the file to one of four categories (vehicle, pedestrian, road, lane)
$ sh run_test.sh
Test Pre-trained Models
To test the pre-trained models, first download the pre-trained model parameters from Here. Next, copy them into 'saved_models' folder. Finally, edit the parameter 'exp_id' in 'run_test.sh' file to match the downloaded experiment id and run the followings. It is worth nothing that we trained our models four times each of which corresponds to one of the four classes.
$ python run_test.sh
Paper Download
Arxiv
Citation
@InProceedings{Choi,
author = {D. Choi and J. Kang and T. An and K. An and K. Min},
title = {Progressive Query Refinement Framework for Bird's-Eye-View Semantic Segmentation from Surrounding Images},
booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year = {2024}
}

Acknowledgement
We would like to thank the authors of CVT, BEVFormer, FIERY, LSS for their sharing the original implementation codes. Our work is highly inspired and motivated not only by the mentioned works but also by other predecessors.
About
The official implementation of "Progressive Query Refinement Frame for BEV semantic segmentation from surrounding images" to be presented in IROS 2024.
Topics
autonomous-drivingbevsemantic-segmentationsurrounding-images
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages