|
|
<!DOCTYPE html> |
|
|
<html lang="en"> |
|
|
<head> |
|
|
<meta charset="UTF-8"> |
|
|
<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
|
|
<title>🤖 Open Multi-Agent Collaborative Perception, Prediction, and Planning</title> |
|
|
<link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css" rel="stylesheet"> |
|
|
<style> |
|
|
* { |
|
|
margin: 0; |
|
|
padding: 0; |
|
|
box-sizing: border-box; |
|
|
} |
|
|
|
|
|
body { |
|
|
font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif; |
|
|
background: linear-gradient(135deg, #070707 0%, #20033d 100%); |
|
|
min-height: 100vh; |
|
|
color: #333; |
|
|
} |
|
|
|
|
|
.container { |
|
|
max-width: 1400px; |
|
|
margin: 0 auto; |
|
|
padding: 20px; |
|
|
} |
|
|
|
|
|
.header { |
|
|
text-align: center; |
|
|
margin-bottom: 40px; |
|
|
color: white; |
|
|
} |
|
|
|
|
|
.header h1 { |
|
|
font-size: 3rem; |
|
|
font-weight: 700; |
|
|
margin-bottom: 10px; |
|
|
text-shadow: 2px 2px 4px rgba(0,0,0,0.3); |
|
|
} |
|
|
|
|
|
.header p { |
|
|
font-size: 1.2rem; |
|
|
opacity: 0.9; |
|
|
margin-bottom: 20px; |
|
|
} |
|
|
|
|
|
.stats-bar { |
|
|
display: flex; |
|
|
justify-content: center; |
|
|
gap: 30px; |
|
|
margin: 20px 0; |
|
|
flex-wrap: wrap; |
|
|
} |
|
|
|
|
|
.stat-badge { |
|
|
background: rgba(255,255,255,0.2); |
|
|
padding: 10px 20px; |
|
|
border-radius: 25px; |
|
|
color: white; |
|
|
font-weight: bold; |
|
|
text-align: center; |
|
|
} |
|
|
|
|
|
.stat-number { |
|
|
font-size: 1.5rem; |
|
|
display: block; |
|
|
} |
|
|
|
|
|
.main-sections { |
|
|
display: grid; |
|
|
grid-template-columns: repeat(auto-fit, minmax(350px, 1fr)); |
|
|
gap: 30px; |
|
|
margin-bottom: 40px; |
|
|
} |
|
|
|
|
|
.section-card { |
|
|
background: rgba(255,255,255,0.95); |
|
|
border-radius: 20px; |
|
|
padding: 40px; |
|
|
cursor: pointer; |
|
|
transition: all 0.3s ease; |
|
|
text-align: center; |
|
|
box-shadow: 0 10px 30px rgba(0,0,0,0.1); |
|
|
position: relative; |
|
|
overflow: hidden; |
|
|
} |
|
|
|
|
|
.section-card::before { |
|
|
content: ''; |
|
|
position: absolute; |
|
|
top: 0; |
|
|
left: -100%; |
|
|
width: 100%; |
|
|
height: 100%; |
|
|
background: linear-gradient(90deg, transparent, rgba(255,255,255,0.4), transparent); |
|
|
transition: left 0.5s ease; |
|
|
} |
|
|
|
|
|
.section-card:hover::before { |
|
|
left: 100%; |
|
|
} |
|
|
|
|
|
.section-card:hover { |
|
|
transform: translateY(-10px); |
|
|
box-shadow: 0 20px 40px rgba(0,0,0,0.15); |
|
|
} |
|
|
|
|
|
.section-icon { |
|
|
font-size: 4rem; |
|
|
margin-bottom: 20px; |
|
|
color: #667eea; |
|
|
} |
|
|
|
|
|
.perception-card .section-icon { color: #FF6B6B; } |
|
|
.tracking-card .section-icon { color: #4ECDC4; } |
|
|
.prediction-card .section-icon { color: #45B7D1; } |
|
|
.datasets-card .section-icon { color: #96CEB4; } |
|
|
.methods-card .section-icon { color: #FECA57; } |
|
|
.conferences-card .section-icon { color: #A55EEA; } |
|
|
|
|
|
.section-card h2 { |
|
|
font-size: 1.8rem; |
|
|
margin-bottom: 15px; |
|
|
color: #333; |
|
|
} |
|
|
|
|
|
.section-card p { |
|
|
color: #666; |
|
|
font-size: 1rem; |
|
|
line-height: 1.6; |
|
|
margin-bottom: 20px; |
|
|
} |
|
|
|
|
|
.stats { |
|
|
display: flex; |
|
|
justify-content: space-around; |
|
|
margin-top: 20px; |
|
|
} |
|
|
|
|
|
.stat { |
|
|
text-align: center; |
|
|
} |
|
|
|
|
|
.stat-number { |
|
|
font-size: 1.3rem; |
|
|
font-weight: bold; |
|
|
color: #667eea; |
|
|
} |
|
|
|
|
|
.stat-label { |
|
|
font-size: 0.8rem; |
|
|
color: #666; |
|
|
} |
|
|
|
|
|
.content-panel { |
|
|
display: none; |
|
|
background: rgba(255,255,255,0.95); |
|
|
border-radius: 20px; |
|
|
padding: 30px; |
|
|
margin-top: 20px; |
|
|
box-shadow: 0 10px 30px rgba(0,0,0,0.1); |
|
|
} |
|
|
|
|
|
.content-panel.active { |
|
|
display: block; |
|
|
animation: slideIn 0.3s ease; |
|
|
} |
|
|
|
|
|
@keyframes slideIn { |
|
|
from { opacity: 0; transform: translateY(20px); } |
|
|
to { opacity: 1; transform: translateY(0); } |
|
|
} |
|
|
|
|
|
.panel-header { |
|
|
display: flex; |
|
|
justify-content: space-between; |
|
|
align-items: center; |
|
|
margin-bottom: 30px; |
|
|
padding-bottom: 15px; |
|
|
border-bottom: 2px solid #eee; |
|
|
} |
|
|
|
|
|
.panel-title { |
|
|
font-size: 2rem; |
|
|
color: #333; |
|
|
} |
|
|
|
|
|
.close-btn { |
|
|
background: #ff4757; |
|
|
color: white; |
|
|
border: none; |
|
|
border-radius: 50%; |
|
|
width: 40px; |
|
|
height: 40px; |
|
|
cursor: pointer; |
|
|
font-size: 1.2rem; |
|
|
transition: all 0.3s ease; |
|
|
} |
|
|
|
|
|
.close-btn:hover { |
|
|
background: #ff3838; |
|
|
transform: scale(1.1); |
|
|
} |
|
|
|
|
|
.papers-grid { |
|
|
display: grid; |
|
|
grid-template-columns: repeat(auto-fit, minmax(400px, 1fr)); |
|
|
gap: 20px; |
|
|
margin-bottom: 30px; |
|
|
} |
|
|
|
|
|
.paper-item { |
|
|
background: #f8f9fa; |
|
|
border-radius: 15px; |
|
|
padding: 20px; |
|
|
border-left: 4px solid #667eea; |
|
|
transition: all 0.3s ease; |
|
|
} |
|
|
|
|
|
.paper-item:hover { |
|
|
transform: translateX(5px); |
|
|
background: #e3f2fd; |
|
|
box-shadow: 0 5px 15px rgba(0,0,0,0.1); |
|
|
} |
|
|
|
|
|
.paper-venue { |
|
|
background: #667eea; |
|
|
color: white; |
|
|
padding: 4px 12px; |
|
|
border-radius: 15px; |
|
|
font-size: 0.8rem; |
|
|
font-weight: bold; |
|
|
display: inline-block; |
|
|
margin-bottom: 10px; |
|
|
} |
|
|
|
|
|
.paper-title { |
|
|
font-size: 1.1rem; |
|
|
font-weight: 600; |
|
|
color: #333; |
|
|
margin-bottom: 8px; |
|
|
} |
|
|
|
|
|
.paper-description { |
|
|
color: #666; |
|
|
font-size: 0.9rem; |
|
|
line-height: 1.4; |
|
|
margin-bottom: 15px; |
|
|
} |
|
|
|
|
|
.paper-links { |
|
|
display: flex; |
|
|
gap: 10px; |
|
|
flex-wrap: wrap; |
|
|
} |
|
|
|
|
|
.link-btn { |
|
|
background: linear-gradient(45deg, #667eea, #764ba2); |
|
|
color: white; |
|
|
border: none; |
|
|
padding: 6px 12px; |
|
|
border-radius: 15px; |
|
|
cursor: pointer; |
|
|
font-size: 0.8rem; |
|
|
text-decoration: none; |
|
|
display: inline-flex; |
|
|
align-items: center; |
|
|
gap: 5px; |
|
|
transition: all 0.3s ease; |
|
|
} |
|
|
|
|
|
.link-btn:hover { |
|
|
transform: translateY(-2px); |
|
|
box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4); |
|
|
} |
|
|
|
|
|
.link-btn.code { background: linear-gradient(45deg, #4ECDC4, #44A08D); } |
|
|
.link-btn.project { background: linear-gradient(45deg, #FF6B6B, #ee5a52); } |
|
|
|
|
|
.search-container { |
|
|
margin-bottom: 30px; |
|
|
} |
|
|
|
|
|
.search-box { |
|
|
width: 100%; |
|
|
max-width: 500px; |
|
|
margin: 0 auto; |
|
|
display: block; |
|
|
padding: 15px 20px; |
|
|
border: none; |
|
|
border-radius: 25px; |
|
|
font-size: 1rem; |
|
|
box-shadow: 0 5px 15px rgba(0,0,0,0.1); |
|
|
outline: none; |
|
|
} |
|
|
|
|
|
.filter-buttons { |
|
|
display: flex; |
|
|
justify-content: center; |
|
|
gap: 10px; |
|
|
margin: 20px 0; |
|
|
flex-wrap: wrap; |
|
|
} |
|
|
|
|
|
.filter-btn { |
|
|
background: rgba(255,255,255,0.9); |
|
|
border: 2px solid #667eea; |
|
|
color: #667eea; |
|
|
padding: 8px 16px; |
|
|
border-radius: 20px; |
|
|
cursor: pointer; |
|
|
transition: all 0.3s ease; |
|
|
} |
|
|
|
|
|
.filter-btn.active, |
|
|
.filter-btn:hover { |
|
|
background: #667eea; |
|
|
color: white; |
|
|
} |
|
|
|
|
|
.back-to-top { |
|
|
position: fixed; |
|
|
bottom: 30px; |
|
|
right: 30px; |
|
|
background: #667eea; |
|
|
color: white; |
|
|
border: none; |
|
|
border-radius: 50%; |
|
|
width: 50px; |
|
|
height: 50px; |
|
|
cursor: pointer; |
|
|
font-size: 1.2rem; |
|
|
box-shadow: 0 5px 15px rgba(0,0,0,0.2); |
|
|
transition: all 0.3s ease; |
|
|
opacity: 0; |
|
|
visibility: hidden; |
|
|
} |
|
|
|
|
|
.back-to-top.visible { |
|
|
opacity: 1; |
|
|
visibility: visible; |
|
|
} |
|
|
|
|
|
.back-to-top:hover { |
|
|
transform: translateY(-3px); |
|
|
background: #5a67d8; |
|
|
} |
|
|
|
|
|
@media (max-width: 768px) { |
|
|
.main-sections { |
|
|
grid-template-columns: 1fr; |
|
|
gap: 20px; |
|
|
} |
|
|
|
|
|
.header h1 { |
|
|
font-size: 2rem; |
|
|
} |
|
|
|
|
|
.papers-grid { |
|
|
grid-template-columns: 1fr; |
|
|
} |
|
|
|
|
|
.section-card { |
|
|
padding: 30px 20px; |
|
|
} |
|
|
|
|
|
.stats-bar { |
|
|
gap: 15px; |
|
|
} |
|
|
|
|
|
.stat-badge { |
|
|
padding: 8px 15px; |
|
|
font-size: 0.9rem; |
|
|
} |
|
|
} |
|
|
|
|
|
.dataset-table { |
|
|
width: 100%; |
|
|
border-collapse: collapse; |
|
|
margin: 20px 0; |
|
|
background: white; |
|
|
border-radius: 10px; |
|
|
overflow: hidden; |
|
|
box-shadow: 0 5px 15px rgba(0,0,0,0.1); |
|
|
} |
|
|
|
|
|
.dataset-table th, |
|
|
.dataset-table td { |
|
|
padding: 12px 15px; |
|
|
text-align: left; |
|
|
border-bottom: 1px solid #eee; |
|
|
} |
|
|
|
|
|
.dataset-table th { |
|
|
background: #667eea; |
|
|
color: white; |
|
|
font-weight: 600; |
|
|
} |
|
|
|
|
|
.dataset-table tr:hover { |
|
|
background: #f8f9fa; |
|
|
} |
|
|
|
|
|
.tag { |
|
|
background: #e3f2fd; |
|
|
color: #1976d2; |
|
|
padding: 2px 8px; |
|
|
border-radius: 10px; |
|
|
font-size: 0.8rem; |
|
|
margin: 2px; |
|
|
display: inline-block; |
|
|
} |
|
|
</style> |
|
|
</head> |
|
|
<body> |
|
|
<div class="container"> |
|
|
<div class="header"> |
|
|
<h1><i class="fas fa-robot"></i> Awesome Multi-Agent Collaborative Perception</h1> |
|
|
<p>Explore cutting-edge resources for Multi-Agent Collaborative Perception, Prediction, and Planning</p> |
|
|
|
|
|
<div class="stats-bar"> |
|
|
<div class="stat-badge"> |
|
|
<span class="stat-number">200+</span> |
|
|
<span>Papers</span> |
|
|
</div> |
|
|
<div class="stat-badge"> |
|
|
<span class="stat-number">25+</span> |
|
|
<span>Datasets</span> |
|
|
</div> |
|
|
<div class="stat-badge"> |
|
|
<span class="stat-number">50+</span> |
|
|
<span>Code Repos</span> |
|
|
</div> |
|
|
<div class="stat-badge"> |
|
|
<span class="stat-number">2025</span> |
|
|
<span>Updated</span> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div class="main-sections"> |
|
|
<div class="section-card perception-card" onclick="showContent('perception')"> |
|
|
<div class="section-icon"> |
|
|
<i class="fas fa-eye"></i> |
|
|
</div> |
|
|
<h2>🔍 Perception</h2> |
|
|
<p>Multi-agent collaborative sensing, 3D object detection, semantic segmentation, and sensor fusion techniques for enhanced environmental understanding.</p> |
|
|
<div class="stats"> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">80+</div> |
|
|
<div class="stat-label">Papers</div> |
|
|
</div> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">V2X</div> |
|
|
<div class="stat-label">Focus</div> |
|
|
</div> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">15+</div> |
|
|
<div class="stat-label">Venues</div> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div class="section-card tracking-card" onclick="showContent('tracking')"> |
|
|
<div class="section-icon"> |
|
|
<i class="fas fa-route"></i> |
|
|
</div> |
|
|
<h2>📍 Tracking</h2> |
|
|
<p>Multi-object tracking, collaborative state estimation, uncertainty quantification, and temporal consistency across multiple agents.</p> |
|
|
<div class="stats"> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">15+</div> |
|
|
<div class="stat-label">Methods</div> |
|
|
</div> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">MOT</div> |
|
|
<div class="stat-label">Focus</div> |
|
|
</div> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">5+</div> |
|
|
<div class="stat-label">Datasets</div> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div class="section-card prediction-card" onclick="showContent('prediction')"> |
|
|
<div class="section-icon"> |
|
|
<i class="fas fa-chart-line"></i> |
|
|
</div> |
|
|
<h2>🔮 Prediction</h2> |
|
|
<p>Trajectory forecasting, motion prediction, behavior understanding, and cooperative planning for autonomous systems.</p> |
|
|
<div class="stats"> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">25+</div> |
|
|
<div class="stat-label">Papers</div> |
|
|
</div> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">GNN</div> |
|
|
<div class="stat-label">Core Tech</div> |
|
|
</div> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">E2E</div> |
|
|
<div class="stat-label">Systems</div> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div class="section-card datasets-card" onclick="showContent('datasets')"> |
|
|
<div class="section-icon"> |
|
|
<i class="fas fa-database"></i> |
|
|
</div> |
|
|
<h2>📊 Datasets</h2> |
|
|
<p>Real-world and simulated datasets for collaborative perception research, including benchmarks and evaluation protocols.</p> |
|
|
<div class="stats"> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">25+</div> |
|
|
<div class="stat-label">Datasets</div> |
|
|
</div> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">Real</div> |
|
|
<div class="stat-label">& Sim</div> |
|
|
</div> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">3D</div> |
|
|
<div class="stat-label">Labels</div> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div class="section-card methods-card" onclick="showContent('methods')"> |
|
|
<div class="section-icon"> |
|
|
<i class="fas fa-cogs"></i> |
|
|
</div> |
|
|
<h2>⚙️ Methods</h2> |
|
|
<p>Communication strategies, fusion techniques, robustness approaches, and learning paradigms for multi-agent systems.</p> |
|
|
<div class="stats"> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">60+</div> |
|
|
<div class="stat-label">Methods</div> |
|
|
</div> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">Comm</div> |
|
|
<div class="stat-label">Efficient</div> |
|
|
</div> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">Robust</div> |
|
|
<div class="stat-label">Defense</div> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div class="section-card conferences-card" onclick="showContent('conferences')"> |
|
|
<div class="section-icon"> |
|
|
<i class="fas fa-university"></i> |
|
|
</div> |
|
|
<h2>🏛️ Conferences</h2> |
|
|
<p>Top-tier venues, workshops, and publication trends in collaborative perception and multi-agent systems research.</p> |
|
|
<div class="stats"> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">10+</div> |
|
|
<div class="stat-label">Venues</div> |
|
|
</div> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">2025</div> |
|
|
<div class="stat-label">Latest</div> |
|
|
</div> |
|
|
<div class="stat"> |
|
|
<div class="stat-number">Trend</div> |
|
|
<div class="stat-label">Analysis</div> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
|
|
|
<div id="perceptionPanel" class="content-panel"> |
|
|
<div class="panel-header"> |
|
|
<h2 class="panel-title"><i class="fas fa-eye"></i> Collaborative Perception</h2> |
|
|
<button class="close-btn" onclick="hideContent()"> |
|
|
<i class="fas fa-times"></i> |
|
|
</button> |
|
|
</div> |
|
|
|
|
|
<div class="search-container"> |
|
|
<input type="text" class="search-box" placeholder="Search perception papers..." onkeyup="filterPapers('perception')"> |
|
|
</div> |
|
|
|
|
|
<div class="filter-buttons"> |
|
|
<button class="filter-btn active" onclick="filterByVenue('perception', 'all')">All</button> |
|
|
<button class="filter-btn" onclick="filterByVenue('perception', 'CVPR 2025')">CVPR 2025</button> |
|
|
<button class="filter-btn" onclick="filterByVenue('perception', 'ICLR 2025')">ICLR 2025</button> |
|
|
<button class="filter-btn" onclick="filterByVenue('perception', 'AAAI 2025')">AAAI 2025</button> |
|
|
<button class="filter-btn" onclick="filterByVenue('perception', 'NeurIPS')">NeurIPS</button> |
|
|
</div> |
|
|
|
|
|
<div id="perceptionPapers" class="papers-grid"> |
|
|
|
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div id="trackingPanel" class="content-panel"> |
|
|
<div class="panel-header"> |
|
|
<h2 class="panel-title"><i class="fas fa-route"></i> Collaborative Tracking</h2> |
|
|
<button class="close-btn" onclick="hideContent()"> |
|
|
<i class="fas fa-times"></i> |
|
|
</button> |
|
|
</div> |
|
|
|
|
|
<div class="search-container"> |
|
|
<input type="text" class="search-box" placeholder="Search tracking papers..." onkeyup="filterPapers('tracking')"> |
|
|
</div> |
|
|
|
|
|
<div id="trackingPapers" class="papers-grid"> |
|
|
|
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div id="predictionPanel" class="content-panel"> |
|
|
<div class="panel-header"> |
|
|
<h2 class="panel-title"><i class="fas fa-chart-line"></i> Collaborative Prediction</h2> |
|
|
<button class="close-btn" onclick="hideContent()"> |
|
|
<i class="fas fa-times"></i> |
|
|
</button> |
|
|
</div> |
|
|
|
|
|
<div class="search-container"> |
|
|
<input type="text" class="search-box" placeholder="Search prediction papers..." onkeyup="filterPapers('prediction')"> |
|
|
</div> |
|
|
|
|
|
<div id="predictionPapers" class="papers-grid"> |
|
|
|
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div id="datasetsPanel" class="content-panel"> |
|
|
<div class="panel-header"> |
|
|
<h2 class="panel-title"><i class="fas fa-database"></i> Datasets & Benchmarks</h2> |
|
|
<button class="close-btn" onclick="hideContent()"> |
|
|
<i class="fas fa-times"></i> |
|
|
</button> |
|
|
</div> |
|
|
|
|
|
<div class="search-container"> |
|
|
<input type="text" class="search-box" placeholder="Search datasets..." onkeyup="filterDatasets()"> |
|
|
</div> |
|
|
|
|
|
<div class="filter-buttons"> |
|
|
<button class="filter-btn active" onclick="filterDatasetType('all')">All</button> |
|
|
<button class="filter-btn" onclick="filterDatasetType('real')">Real-World</button> |
|
|
<button class="filter-btn" onclick="filterDatasetType('simulation')">Simulation</button> |
|
|
<button class="filter-btn" onclick="filterDatasetType('v2x')">V2X</button> |
|
|
</div> |
|
|
|
|
|
<table class="dataset-table" id="datasetsTable"> |
|
|
<thead> |
|
|
<tr> |
|
|
<th>Dataset</th> |
|
|
<th>Year</th> |
|
|
<th>Type</th> |
|
|
<th>Agents</th> |
|
|
<th>Size</th> |
|
|
<th>Features</th> |
|
|
<th>Access</th> |
|
|
</tr> |
|
|
</thead> |
|
|
<tbody> |
|
|
|
|
|
</tbody> |
|
|
</table> |
|
|
</div> |
|
|
|
|
|
<div id="methodsPanel" class="content-panel"> |
|
|
<div class="panel-header"> |
|
|
<h2 class="panel-title"><i class="fas fa-cogs"></i> Methods & Techniques</h2> |
|
|
<button class="close-btn" onclick="hideContent()"> |
|
|
<i class="fas fa-times"></i> |
|
|
</button> |
|
|
</div> |
|
|
|
|
|
<div class="search-container"> |
|
|
<input type="text" class="search-box" placeholder="Search methods..." onkeyup="filterPapers('methods')"> |
|
|
</div> |
|
|
|
|
|
<div class="filter-buttons"> |
|
|
<button class="filter-btn active" onclick="filterMethodType('all')">All</button> |
|
|
<button class="filter-btn" onclick="filterMethodType('communication')">Communication</button> |
|
|
<button class="filter-btn" onclick="filterMethodType('robustness')">Robustness</button> |
|
|
<button class="filter-btn" onclick="filterMethodType('learning')">Learning</button> |
|
|
</div> |
|
|
|
|
|
<div id="methodsPapers" class="papers-grid"> |
|
|
|
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div id="conferencesPanel" class="content-panel"> |
|
|
<div class="panel-header"> |
|
|
<h2 class="panel-title"><i class="fas fa-university"></i> Conferences & Venues</h2> |
|
|
<button class="close-btn" onclick="hideContent()"> |
|
|
<i class="fas fa-times"></i> |
|
|
</button> |
|
|
</div> |
|
|
|
|
|
<div id="conferencesContent"> |
|
|
|
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<button class="back-to-top" id="backToTop" onclick="scrollToTop()"> |
|
|
<i class="fas fa-arrow-up"></i> |
|
|
</button> |
|
|
|
|
|
<script> |
|
|
|
|
|
const perceptionPapers = [ |
|
|
{ |
|
|
title: "CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness", |
|
|
venue: "CVPR 2025", |
|
|
description: "Novel approach for efficient collaborative perception using supply-demand awareness and intermediate-late hybridization.", |
|
|
paper: "https://arxiv.org/abs/2503.03430", |
|
|
code: "https://github.com/Xu2729/CoSDH", |
|
|
project: null |
|
|
}, |
|
|
{ |
|
|
title: "V2X-R: Cooperative LiDAR-4D Radar Fusion for 3D Object Detection", |
|
|
venue: "CVPR 2025", |
|
|
description: "Cooperative fusion of LiDAR and 4D radar sensors for enhanced 3D object detection with denoising diffusion.", |
|
|
paper: "https://arxiv.org/abs/2411.08402", |
|
|
code: "https://github.com/ylwhxht/V2X-R", |
|
|
project: null |
|
|
}, |
|
|
{ |
|
|
title: "STAMP: Scalable Task- And Model-Agnostic Collaborative Perception", |
|
|
venue: "ICLR 2025", |
|
|
description: "Framework for scalable collaborative perception that is both task and model agnostic.", |
|
|
paper: "https://openreview.net/forum?id=8NdNniulYE", |
|
|
code: "https://github.com/taco-group/STAMP", |
|
|
project: null |
|
|
}, |
|
|
{ |
|
|
title: "Where2comm: Efficient Collaborative Perception via Spatial Confidence Maps", |
|
|
venue: "NeurIPS 2022", |
|
|
description: "Groundbreaking work on efficient collaborative perception using spatial confidence maps for selective communication.", |
|
|
paper: "https://openreview.net/forum?id=dLL4KXzKUpS", |
|
|
code: "https://github.com/MediaBrain-SJTU/where2comm", |
|
|
project: null |
|
|
}, |
|
|
{ |
|
|
title: "CoBEVFlow: Robust Asynchronous Collaborative 3D Detection via Bird's Eye View Flow", |
|
|
venue: "NeurIPS 2023", |
|
|
description: "Handles temporal asynchrony in collaborative perception using bird's eye view flow.", |
|
|
paper: "https://openreview.net/forum?id=UHIDdtxmVS", |
|
|
code: "https://github.com/MediaBrain-SJTU/CoBEVFlow", |
|
|
project: null |
|
|
}, |
|
|
{ |
|
|
title: "UniV2X: End-to-End Autonomous Driving through V2X Cooperation", |
|
|
venue: "AAAI 2025", |
|
|
description: "Complete end-to-end system for autonomous driving with V2X cooperation.", |
|
|
paper: "https://arxiv.org/abs/2404.00717", |
|
|
code: "https://github.com/AIR-THU/UniV2X", |
|
|
project: null |
|
|
} |
|
|
]; |
|
|
|
|
|
const trackingPapers = [ |
|
|
{ |
|
|
title: "MOT-CUP: Multi-Object Tracking with Conformal Uncertainty Propagation", |
|
|
venue: "Preprint", |
|
|
description: "Collaborative multi-object tracking with conformal uncertainty propagation for robust state estimation.", |
|
|
paper: "https://arxiv.org/abs/2303.14346", |
|
|
code: "https://github.com/susanbao/mot_cup", |
|
|
project: null |
|
|
}, |
|
|
{ |
|
|
title: "DMSTrack: Probabilistic 3D Multi-Object Cooperative Tracking", |
|
|
venue: "ICRA 2024", |
|
|
description: "Probabilistic approach for 3D multi-object cooperative tracking using differentiable multi-sensor Kalman filter.", |
|
|
paper: "https://arxiv.org/abs/2309.14655", |
|
|
code: "https://github.com/eddyhkchiu/DMSTrack", |
|
|
project: null |
|
|
}, |
|
|
{ |
|
|
title: "CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic Feature Trust", |
|
|
venue: "ICRA 2025", |
|
|
description: "Dynamic feature trust modulus for robust asynchronous collaborative perception.", |
|
|
paper: "https://arxiv.org/abs/2502.08169", |
|
|
code: "https://github.com/CrazyShout/CoDynTrust", |
|
|
project: null |
|
|
} |
|
|
]; |
|
|
|
|
|
const predictionPapers = [ |
|
|
{ |
|
|
title: "V2X-Graph: Learning Cooperative Trajectory Representations", |
|
|
venue: "NeurIPS 2024", |
|
|
description: "Graph neural networks for learning cooperative trajectory representations in multi-agent systems.", |
|
|
paper: "https://arxiv.org/abs/2311.00371", |
|
|
code: "https://github.com/AIR-THU/V2X-Graph", |
|
|
project: null |
|
|
}, |
|
|
{ |
|
|
title: "Co-MTP: Cooperative Trajectory Prediction Framework", |
|
|
venue: "ICRA 2025", |
|
|
description: "Multi-temporal fusion framework for cooperative trajectory prediction in autonomous driving.", |
|
|
paper: "https://arxiv.org/abs/2502.16589", |
|
|
code: "https://github.com/xiaomiaozhang/Co-MTP", |
|
|
project: null |
|
|
}, |
|
|
{ |
|
|
title: "V2XPnP: Vehicle-to-Everything Spatio-Temporal Fusion", |
|
|
venue: "Preprint", |
|
|
description: "Spatio-temporal fusion approach for multi-agent perception and prediction in V2X systems.", |
|
|
paper: "https://arxiv.org/abs/2412.01812", |
|
|
code: "https://github.com/Zewei-Zhou/V2XPnP", |
|
|
project: null |
|
|
} |
|
|
]; |
|
|
|
|
|
const datasets = [ |
|
|
{ |
|
|
name: "DAIR-V2X", |
|
|
year: "2022", |
|
|
type: "real", |
|
|
agents: "V2I", |
|
|
size: "71K frames", |
|
|
features: ["3D boxes", "Multi-modal", "Infrastructure"], |
|
|
link: "https://github.com/AIR-THU/DAIR-V2X" |
|
|
}, |
|
|
{ |
|
|
name: "V2V4Real", |
|
|
year: "2023", |
|
|
type: "real", |
|
|
agents: "V2V", |
|
|
size: "20K frames", |
|
|
features: ["3D boxes", "Real V2V", "Highway"], |
|
|
link: "https://github.com/ucla-mobility/V2V4Real" |
|
|
}, |
|
|
{ |
|
|
name: "TUMTraf-V2X", |
|
|
year: "2024", |
|
|
type: "real", |
|
|
agents: "V2X", |
|
|
size: "2K sequences", |
|
|
features: ["Dense labels", "Cooperative", "Urban"], |
|
|
link: "https://github.com/tum-traffic-dataset/tum-traffic-dataset-dev-kit" |
|
|
}, |
|
|
{ |
|
|
name: "OPV2V", |
|
|
year: "2022", |
|
|
type: "simulation", |
|
|
agents: "V2V", |
|
|
size: "Large-scale", |
|
|
features: ["CARLA", "Multi-agent", "Benchmark"], |
|
|
link: "https://github.com/DerrickXuNu/OpenCOOD" |
|
|
}, |
|
|
{ |
|
|
name: "V2X-Sim", |
|
|
year: "2021", |
|
|
type: "simulation", |
|
|
agents: "Multi", |
|
|
size: "Scalable", |
|
|
features: ["Multi-agent", "Collaborative", "Synthetic"], |
|
|
link: "https://github.com/ai4ce/V2X-Sim" |
|
|
} |
|
|
]; |
|
|
|
|
|
const methodsPapers = [ |
|
|
{ |
|
|
title: "ACCO: Is Discretization Fusion All You Need?", |
|
|
venue: "Preprint", |
|
|
description: "Investigation of discretization fusion techniques for collaborative perception efficiency.", |
|
|
paper: "https://arxiv.org/abs/2503.13946", |
|
|
code: "https://github.com/sidiangongyuan/ACCO", |
|
|
project: null, |
|
|
category: "communication" |
|
|
}, |
|
|
{ |
|
|
title: "CP-Guard: Malicious Agent Detection and Defense", |
|
|
venue: "AAAI 2025", |
|
|
description: "Comprehensive framework for detecting and defending against malicious agents in collaborative perception.", |
|
|
paper: "https://arxiv.org/abs/2412.12000", |
|
|
code: null, |
|
|
project: null, |
|
|
category: "robustness" |
|
|
}, |
|
|
{ |
|
|
title: "HEAL: Extensible Framework for Heterogeneous Collaborative Perception", |
|
|
venue: "ICLR 2024", |
|
|
description: "Open framework for heterogeneous collaborative perception with extensive customization options.", |
|
|
paper: "https://openreview.net/forum?id=KkrDUGIASk", |
|
|
code: "https://github.com/yifanlu0227/HEAL", |
|
|
project: null, |
|
|
category: "learning" |
|
|
} |
|
|
]; |
|
|
|
|
|
function showContent(section) { |
|
|
|
|
|
document.querySelectorAll('.content-panel').forEach(panel => { |
|
|
panel.classList.remove('active'); |
|
|
}); |
|
|
|
|
|
|
|
|
document.getElementById(section + 'Panel').classList.add('active'); |
|
|
|
|
|
|
|
|
if (section === 'perception') { |
|
|
populatePapers('perceptionPapers', perceptionPapers); |
|
|
} else if (section === 'tracking') { |
|
|
populatePapers('trackingPapers', trackingPapers); |
|
|
} else if (section === 'prediction') { |
|
|
populatePapers('predictionPapers', predictionPapers); |
|
|
} else if (section === 'datasets') { |
|
|
populateDatasets(); |
|
|
} else if (section === 'methods') { |
|
|
populatePapers('methodsPapers', methodsPapers); |
|
|
} else if (section === 'conferences') { |
|
|
populateConferences(); |
|
|
} |
|
|
|
|
|
|
|
|
setTimeout(() => { |
|
|
document.querySelector('.content-panel.active').scrollIntoView({ |
|
|
behavior: 'smooth', |
|
|
block: 'start' |
|
|
}); |
|
|
}, 100); |
|
|
} |
|
|
|
|
|
function hideContent() { |
|
|
document.querySelectorAll('.content-panel').forEach(panel => { |
|
|
panel.classList.remove('active'); |
|
|
}); |
|
|
|
|
|
|
|
|
window.scrollTo({ top: 0, behavior: 'smooth' }); |
|
|
} |
|
|
|
|
|
function populatePapers(containerId, papers) { |
|
|
const container = document.getElementById(containerId); |
|
|
let html = ''; |
|
|
|
|
|
papers.forEach(paper => { |
|
|
html += ` |
|
|
<div class="paper-item" data-venue="${paper.venue}"> |
|
|
<div class="paper-venue">${paper.venue}</div> |
|
|
<div class="paper-title">${paper.title}</div> |
|
|
<div class="paper-description">${paper.description}</div> |
|
|
<div class="paper-links"> |
|
|
<a href="${paper.paper}" class="link-btn" target="_blank"> |
|
|
<i class="fas fa-file-alt"></i> Paper |
|
|
</a> |
|
|
${paper.code ? `<a href="${paper.code}" class="link-btn code" target="_blank"> |
|
|
<i class="fas fa-code"></i> Code |
|
|
</a>` : ''} |
|
|
${paper.project ? `<a href="${paper.project}" class="link-btn project" target="_blank"> |
|
|
<i class="fas fa-globe"></i> Project |
|
|
</a>` : ''} |
|
|
</div> |
|
|
</div> |
|
|
`; |
|
|
}); |
|
|
|
|
|
container.innerHTML = html; |
|
|
} |
|
|
|
|
|
function populateDatasets() { |
|
|
const tbody = document.querySelector('#datasetsTable tbody'); |
|
|
let html = ''; |
|
|
|
|
|
datasets.forEach(dataset => { |
|
|
const featureTags = dataset.features.map(feature => |
|
|
`<span class="tag">${feature}</span>` |
|
|
).join(''); |
|
|
|
|
|
html += ` |
|
|
<tr data-type="${dataset.type}" data-agents="${dataset.agents.toLowerCase()}"> |
|
|
<td><strong>${dataset.name}</strong></td> |
|
|
<td>${dataset.year}</td> |
|
|
<td>${dataset.type === 'real' ? '🌍 Real' : '🎮 Simulation'}</td> |
|
|
<td>${dataset.agents}</td> |
|
|
<td>${dataset.size}</td> |
|
|
<td>${featureTags}</td> |
|
|
<td><a href="${dataset.link}" class="link-btn" target="_blank"> |
|
|
<i class="fas fa-download"></i> Access |
|
|
</a></td> |
|
|
</tr> |
|
|
`; |
|
|
}); |
|
|
|
|
|
tbody.innerHTML = html; |
|
|
} |
|
|
|
|
|
function populateConferences() { |
|
|
const container = document.getElementById('conferencesContent'); |
|
|
container.innerHTML = ` |
|
|
<div class="papers-grid"> |
|
|
<div class="paper-item"> |
|
|
<div class="paper-venue">CVPR 2025</div> |
|
|
<div class="paper-title">Computer Vision and Pattern Recognition</div> |
|
|
<div class="paper-description">5+ collaborative perception papers accepted. Focus on end-to-end systems and robustness.</div> |
|
|
<div class="paper-links"> |
|
|
<a href="#" class="link-btn"> |
|
|
<i class="fas fa-external-link-alt"></i> Conference |
|
|
</a> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div class="paper-item"> |
|
|
<div class="paper-venue">ICLR 2025</div> |
|
|
<div class="paper-title">International Conference on Learning Representations</div> |
|
|
<div class="paper-description">3+ papers accepted. Focus on learning representations and scalability frameworks.</div> |
|
|
<div class="paper-links"> |
|
|
<a href="#" class="link-btn"> |
|
|
<i class="fas fa-external-link-alt"></i> Conference |
|
|
</a> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div class="paper-item"> |
|
|
<div class="paper-venue">ICRA 2025</div> |
|
|
<div class="paper-title">International Conference on Robotics and Automation</div> |
|
|
<div class="paper-description">Robotics-focused collaborative perception. Applications in autonomous driving and UAV swarms.</div> |
|
|
<div class="paper-links"> |
|
|
<a href="#" class="link-btn"> |
|
|
<i class="fas fa-external-link-alt"></i> Conference |
|
|
</a> |
|
|
</div> |
|
|
</div> |
|
|
|
|
|
<div class="paper-item"> |
|
|
<div class="paper-venue">NeurIPS 2024</div> |
|
|
<div class="paper-title">Neural Information Processing Systems</div> |
|
|
<div class="paper-description">Premier venue for machine learning research with strong collaborative perception track record.</div> |
|
|
<div class="paper-links"> |
|
|
<a href="#" class="link-btn"> |
|
|
<i class="fas fa-external-link-alt"></i> Conference |
|
|
</a> |
|
|
</div> |
|
|
</div> |
|
|
</div> |
|
|
`; |
|
|
} |
|
|
|
|
|
function filterByVenue(section, venue) { |
|
|
const buttons = document.querySelectorAll(`#${section}Panel .filter-btn`); |
|
|
buttons.forEach(btn => btn.classList.remove('active')); |
|
|
event.target.classList.add('active'); |
|
|
|
|
|
const papers = document.querySelectorAll(`#${section}Papers .paper-item`); |
|
|
papers.forEach(paper => { |
|
|
if (venue === 'all' || paper.dataset.venue === venue) { |
|
|
paper.style.display = 'block'; |
|
|
} else { |
|
|
paper.style.display = 'none'; |
|
|
} |
|
|
}); |
|
|
} |
|
|
|
|
|
function filterDatasetType(type) { |
|
|
const buttons = document.querySelectorAll('#datasetsPanel .filter-btn'); |
|
|
buttons.forEach(btn => btn.classList.remove('active')); |
|
|
event.target.classList.add('active'); |
|
|
|
|
|
const rows = document.querySelectorAll('#datasetsTable tbody tr'); |
|
|
rows.forEach(row => { |
|
|
if (type === 'all' || |
|
|
row.dataset.type === type || |
|
|
(type === 'v2x' && row.dataset.agents.includes('v'))) { |
|
|
row.style.display = 'table-row'; |
|
|
} else { |
|
|
row.style.display = 'none'; |
|
|
} |
|
|
}); |
|
|
} |
|
|
|
|
|
function filterMethodType(type) { |
|
|
const buttons = document.querySelectorAll('#methodsPanel .filter-btn'); |
|
|
buttons.forEach(btn => btn.classList.remove('active')); |
|
|
event.target.classList.add('active'); |
|
|
|
|
|
|
|
|
|
|
|
} |
|
|
|
|
|
function filterPapers(section) { |
|
|
const searchTerm = event.target.value.toLowerCase(); |
|
|
const papers = document.querySelectorAll(`#${section}Papers .paper-item`); |
|
|
|
|
|
papers.forEach(paper => { |
|
|
const title = paper.querySelector('.paper-title').textContent.toLowerCase(); |
|
|
const description = paper.querySelector('.paper-description').textContent.toLowerCase(); |
|
|
|
|
|
if (title.includes(searchTerm) || description.includes(searchTerm)) { |
|
|
paper.style.display = 'block'; |
|
|
} else { |
|
|
paper.style.display = 'none'; |
|
|
} |
|
|
}); |
|
|
} |
|
|
|
|
|
function filterDatasets() { |
|
|
const searchTerm = event.target.value.toLowerCase(); |
|
|
const rows = document.querySelectorAll('#datasetsTable tbody tr'); |
|
|
|
|
|
rows.forEach(row => { |
|
|
const text = row.textContent.toLowerCase(); |
|
|
if (text.includes(searchTerm)) { |
|
|
row.style.display = 'table-row'; |
|
|
} else { |
|
|
row.style.display = 'none'; |
|
|
} |
|
|
}); |
|
|
} |
|
|
|
|
|
function scrollToTop() { |
|
|
window.scrollTo({ top: 0, behavior: 'smooth' }); |
|
|
} |
|
|
|
|
|
|
|
|
window.addEventListener('scroll', function() { |
|
|
const backToTop = document.getElementById('backToTop'); |
|
|
if (window.pageYOffset > 300) { |
|
|
backToTop.classList.add('visible'); |
|
|
} else { |
|
|
backToTop.classList.remove('visible'); |
|
|
} |
|
|
}); |
|
|
|
|
|
|
|
|
document.addEventListener('DOMContentLoaded', function() { |
|
|
|
|
|
}); |
|
|
</script> |
|
|
</body> |
|
|
</html> |
