https://zhaoylee.github.io/Blogs/categories/transformer-based/ Recent content in Transformer-Based on zhaoyli's Blog Hugo zh-cn [©2024 zhaoyli&rsquo;s Blog] https://zhaoylee.github.io/) Mon, 16 Mar 2026 01:45:51 +0000 https://zhaoylee.github.io/Blogs/posts/plug_and_play/streampetr-qaf2d--enhancing-3d-object-detection-with-2d-detection-guided-query-anchors/ Sun, 15 Mar 2026 21:59:16 +0800 https://zhaoylee.github.io/Blogs/posts/plug_and_play/streampetr-qaf2d--enhancing-3d-object-detection-with-2d-detection-guided-query-anchors/ <hr> <blockquote> <p><strong>🏛️ 会议/期刊</strong>：CVPR<br> <strong>📅 发表年份</strong>：2024<br> <strong>💻 开源代码</strong>：<a href="https://github.com/nullmax-vision/QAF2D">nullmax-vision/QAF2D-CVPR 2024</a><br> <strong>📄 论文题目</strong>：<a href="https://arxiv.org/pdf/2403.06093">Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors</a></p> </blockquote> <hr> <p>这篇发表于 CVPR 2024 的论文 <strong>《Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors》(简称 QAF2D)</strong> 极具工程实用价值。它没有死磕 3D 空间中的特征提取瓶颈，而是打出了一套极其聪明的“降维组合拳”，巧妙地利用成熟的 2D 视觉技术来为 3D 检测器“引路”。</p> https://zhaoylee.github.io/Blogs/posts/plug_and_play/plot---pseudo-labeling-via-object-tracking-for-monocular-3d-object-detection/ Sun, 15 Mar 2026 20:52:51 +0800 https://zhaoylee.github.io/Blogs/posts/plug_and_play/plot---pseudo-labeling-via-object-tracking-for-monocular-3d-object-detection/ <hr> <blockquote> <p><strong>🏛️ 会议/期刊</strong>：ICLR<br> <strong>📅 发表年份</strong>：2026<br> <strong>💻 开源代码</strong>：<a href="%E5%A1%AB%E5%86%99%E4%BD%A0%E7%9A%84URL">无</a><br> <strong>📄 论文题目</strong>：<a href="https://openreview.net/pdf?id=3knS4J9isg">PLOT: Pseudo-Labeling via Object Tracking for 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> 单目 3D 目标检测模型极度“吃数据”。然而，人工标注 3D 边界框极其昂贵且耗时，导致目前带 3D 标签的数据集规模很小，严重限制了模型的泛化能力。</p> https://zhaoylee.github.io/Blogs/posts/transformer-based/monodlgd--difficulty-aware-label-guided-denoising-for-monocular-3d-object-detection/ Sun, 15 Mar 2026 20:52:49 +0800 https://zhaoylee.github.io/Blogs/posts/transformer-based/monodlgd--difficulty-aware-label-guided-denoising-for-monocular-3d-object-detection/ <hr> <blockquote> <p><strong>🏛️ 会议/期刊</strong>：AAAI <br> <strong>📅 发表年份</strong>：2026<br> <strong>💻 开源代码</strong>：<a href="https://github.com/lsy010857/MonoDLGD">MonoDLGD</a><br> <strong>📄 论文题目</strong>：<a href="https://arxiv.org/pdf/2511.13195">Difficulty-Aware Label-Guided Denoising for 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> 在基于 Transformer 的单目 3D 目标检测中，通过向真实标签注入噪声并让模型去重构（即查询去噪 Query Denoising），能有效加速模型收敛并提升几何感知能力。</p> https://zhaoylee.github.io/Blogs/posts/transformer-based/mono3dv--monocular-3d-object-detection-with-3d-aware-bipartite-matching-and-variational-query-denoising/ Sun, 15 Mar 2026 20:36:42 +0800 https://zhaoylee.github.io/Blogs/posts/transformer-based/mono3dv--monocular-3d-object-detection-with-3d-aware-bipartite-matching-and-variational-query-denoising/ <hr> <blockquote> <p><strong>🏛️ 会议/期刊</strong>：CVPR / ICCV / ECCV<br> <strong>📅 发表年份</strong>：2026<br> <strong>💻 开源代码</strong>： 无<br> <strong>📄 论文题目</strong>：<a href="https://arxiv.org/pdf/2601.01036">Mono3DV: Monocular 3D Object Detection with 3D-Aware Bipartite Matching and Variational Query DeNoising</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> 近年来，基于 Transformer（特别是 DETR 架构）的模型在 2D 目标检测中取得了巨大成功，并顺理成章地被引入到单目 3D 目标检测（M3OD）领域。这类模型依赖“查询（Query）”机制和“二分图匹配（Bipartite Matching）”来端到端地输出检测结果，无需繁琐的非极大值抑制（NMS）。</p> https://zhaoylee.github.io/Blogs/posts/plug_and_play/span--spatial-projection-alignment-for-monocular-3d-object-detection/ Sun, 15 Mar 2026 19:38:47 +0800 https://zhaoylee.github.io/Blogs/posts/plug_and_play/span--spatial-projection-alignment-for-monocular-3d-object-detection/ <hr> <blockquote> <p><strong>🏛️ 会议/期刊</strong>：CVPR / ICCV / ECCV<br> <strong>📅 发表年份</strong>：2026<br> <strong>💻 开源代码</strong>：<a href="https://github.com/WYFDUT/SPAN">GitHub 链接</a><br> <strong>📄 论文题目</strong>：<a href="https://arxiv.org/pdf/2511.06702">SPAN: Spatial-Projection Alignment for Monocular 3D Object Detection</a></p> </blockquote> <hr> <h2 id="1-文献背景与研究动机">1. 文献背景与研究动机</h2> <h2 id="背景与现状">背景与现状</h2> <p>单目3D目标检测（Monocular 3D Object Detection）是自动驾驶和机器人视觉中的核心任务，旨在仅通过单张RGB图像预测物体的3D边界框。</p>