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        <title>VLM - 标签 - Zhaoylee&#39;s Blogs</title>
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    <title>Open Vocabulary Monocular 3D Object Detection</title>
    <link>https://zhaoylee.github.io/Blogs_lovelt/posts/open-vocabulary-monocular-3d-object-detection/</link>
    <pubDate>Sun, 15 Mar 2026 21:14:37 &#43;0800</pubDate>
    <author>zhaoylee</author>
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<blockquote>
<p><strong>🏛️ 会议/期刊</strong>：3DV<br>
<strong>📅 发表年份</strong>：2026<br>
<strong>💻 开源代码</strong>：<a href="https://github.com/UVA-Computer-Vision-Lab/ovmono3d" target="_blank" rel="noopener noreffer ">UVA-Computer-Vision-Lab/ovmono3d</a><br>
<strong>📄 论文题目</strong>：<a href="https://arxiv.org/pdf/2411.16833" target="_blank" rel="noopener noreffer ">Open Vocabulary Monocular 3D Object Detection</a></p>
</blockquote>
<hr>
<h3 id="一-背景研究目的与核心问题">一、 背景、研究目的与核心问题</h3>
<ul>
<li>
<p><strong>研究背景：</strong> 传统的单目 3D 目标检测（M3OD）模型都属于“闭集（Closed-set）”学习。这意味着模型只能检测训练集中预先定义好的那几种类别（例如 KITTI 数据集里的车、人、自行车）。但在真实的自动驾驶或机器人场景中，会遇到无数的长尾目标（如遗落的轮胎、奇形怪状的施工路障、甚至是一只突然窜出的动物）。</p>]]></description>
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