https://zhaoylee.github.io/Blogs/categories/%E5%8D%B3%E6%8F%92%E5%8D%B3%E7%94%A8%E5%9E%8B/ Recent content in 即插即用型 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/ocm3d--object-centric-monocular-3d-object-detection/ Mon, 16 Mar 2026 09:12:18 +0800 https://zhaoylee.github.io/Blogs/posts/plug_and_play/ocm3d--object-centric-monocular-3d-object-detection/ <hr> <blockquote> <p><strong>🏛️ 会议/期刊</strong>：arxiv<br> <strong>📅 发表年份</strong>：2021<br> <strong>💻 开源代码</strong>：<a href="https://github.com/mrsempress/OBMO_GUPNet/blob/main/tools/offline_OBMO.py">OBMO_GUPNet</a><br> <strong>📄 论文题目</strong>：<a href="https://arxiv.org/pdf/2104.06041">OCM3D: Object-Centric Monocular 3D Object Detection</a></p> </blockquote> <hr> <h3 id="1-文献背景研究目的与核心问题">1. 文献背景、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（Monocular 3D Object Detection）是一个高度病态（ill-posed）的问题。主流方法通常依赖纯图像或将其转化为伪激光雷达（Pseudo-LiDAR）点云。然而，前者难以捕捉像素间的 3D 空间几何关系，后者则受困于单目深度估计带来的巨大点云噪声。</p> https://zhaoylee.github.io/Blogs/posts/plug_and_play/lr3d--improving-distant-3d-object-detection-using-2d-box-supervision/ Sun, 15 Mar 2026 22:23:00 +0800 https://zhaoylee.github.io/Blogs/posts/plug_and_play/lr3d--improving-distant-3d-object-detection-using-2d-box-supervision/ <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="%E5%A1%AB%E5%86%99%E4%BD%A0%E7%9A%84URL">无</a><br> <strong>📄 论文题目</strong>：<a href="https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Improving_Distant_3D_Object_Detection_Using_2D_Box_Supervision_CVPR_2024_paper.pdf">Improving Distant 3D Object Detection Using 2D Box Supervision</a></p> </blockquote> <hr> <p>这篇由 NVIDIA 等机构的研究人员发表在 CVPR 2024 的重磅论文 <strong>《Improving Distant 3D Object Detection Using 2D Box Supervision》(简称 LR3D)</strong>，切入了一个目前高阶自动驾驶极其头疼的落地难题：<strong>远距离感知（Long-Range Detection）</strong>。它展示了如何用最廉价的标注，榨取单目视觉在远距离上的极限潜力。</p> https://zhaoylee.github.io/Blogs/posts/plug_and_play/obmo--one-bounding-box-multiple-objects-for-monocular-3d-object-detection/ Sun, 15 Mar 2026 21:59:12 +0800 https://zhaoylee.github.io/Blogs/posts/plug_and_play/obmo--one-bounding-box-multiple-objects-for-monocular-3d-object-detection/ <hr> <blockquote> <p><strong>🏛️ 会议/期刊</strong>：IEEE TIP<br> <strong>📅 发表年份</strong>：2023<br> <strong>💻 开源代码</strong>：<a href="https://github.com/mrsempress/OBMO_patchnet">mrsempress/OBMO_patchnet</a><br> <strong>📄 论文题目</strong>：<a href="https://arxiv.org/pdf/2212.10049">OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection</a></p> </blockquote> <hr> <p>这篇发表于 IEEE TIP (2023) 的经典论文 <strong>《OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection》</strong> 切入点非常犀利。它没有在复杂的网络主干上做文章，而是直击单目 3D 目标检测在“底层数学物理逻辑”上的痛点，提出了一种极其优雅的“即插即用（Plug-and-play）”训练策略。</p> https://zhaoylee.github.io/Blogs/posts/plug_and_play/monoxiver--monocular-3d-object-detection-with-bounding-box-denoising-in-3d-by-perceivermonocular-3d-object-detection-with-bounding-box-denoising-in-3d-by-perceiver/ Sun, 15 Mar 2026 21:14:36 +0800 https://zhaoylee.github.io/Blogs/posts/plug_and_play/monoxiver--monocular-3d-object-detection-with-bounding-box-denoising-in-3d-by-perceivermonocular-3d-object-detection-with-bounding-box-denoising-in-3d-by-perceiver/ <hr> <blockquote> <p><strong>🏛️ 会议/期刊</strong>：ICCV<br> <strong>📅 发表年份</strong>：2023<br> <strong>💻 开源代码</strong>：<a href="https://github.com/Xianpeng919/monoxiver">Xianpeng919/monoxiver (ICCV'23)</a><br> <strong>📄 论文题目</strong>：<a href="https://openaccess.thecvf.com/content/ICCV2023/papers/Liu_Monocular_3D_Object_Detection_with_Bounding_Box_Denoising_in_3D_ICCV_2023_paper.pdf">Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver</a></p> </blockquote> <hr> <p>这篇发表于 ICCV 2023 的论文 <strong>《Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver》(简称 MonoXiver)</strong>，提供了一个非常经典且极具工程价值的“自上而下（Top-down）”纠错思路。它并没有试图发明一种全新的主干网络，而是设计了一个强大的“插件”，专门用来拯救那些定位不准的预测框。</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/plug_and_play/dr-traversal-m3d--object-scene-camera-decomposition-and-recomposition-for-data-efficient-monocular-3d-object-detection/ Sun, 15 Mar 2026 19:59:15 +0800 https://zhaoylee.github.io/Blogs/posts/plug_and_play/dr-traversal-m3d--object-scene-camera-decomposition-and-recomposition-for-data-efficient-monocular-3d-object-detection/ <hr> <blockquote> <p><strong>🏛️ 会议/期刊</strong>：IJCV<br> <strong>📅 发表年份</strong>：2026<br> <strong>💻 开源代码</strong>：<a href="https://github.com/kwong292521/DR-Traversal-M3D">GitHub 链接</a><br> <strong>📄 论文题目</strong>：<a href="https://arxiv.org/pdf/2602.20627">Object-Scene-Camera Decomposition and Recomposition for Data-Efficient 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 目标检测本身是一个病态（ill-posed）问题，由于缺乏深度信息，模型极度依赖海量、多样化的训练数据来学习鲁棒的特征。</p> https://zhaoylee.github.io/Blogs/posts/plug_and_play/digging-into-output-representation-for-monocular-3d-object-detection/ Fri, 13 Mar 2026 15:27:43 +0800 https://zhaoylee.github.io/Blogs/posts/plug_and_play/digging-into-output-representation-for-monocular-3d-object-detection/ <hr> <blockquote> <p><strong>🏛️ 会议/期刊</strong>：ICLR<br> <strong>📅 发表年份</strong>：2022<br> <strong>💻 开源代码</strong>：<a href="https://github.com/Owen-Liuyuxuan/visualDet3D/releases/tag/1.1.1">GitHub 链接</a><br> <strong>📄 论文题目</strong>：<a href="https://openreview.net/pdf?id=mPlm356yMIP">DIGGING INTO OUTPUT REPRESENTATION FOR MONOCULAR 3D OBJECT DETECTION</a></p> </blockquote> <hr> <h2 id="0-一句话总结-tldr">0. 一句话总结 (TL;DR)</h2> <p><em>(这篇论文用什么方法，解决了什么问题，达到了什么效果)</em></p>