所有文章 - Zhaoylee's Blogs https://zhaoylee.github.io/Blogs_lovelt/posts/ 所有文章 | Zhaoylee's Blogs Hugo -- gohugo.iozh-CNSat, 04 Apr 2026 12:31:36 +0800 MONOLSS Learnable Sample Selection For Monocular 3D Detection https://zhaoylee.github.io/Blogs_lovelt/posts/monolss--learnable-sample-selection-for-monocular-3d-detection/ Sat, 04 Apr 2026 12:31:36 +0800 zhaoylee https://zhaoylee.github.io/Blogs_lovelt/posts/monolss--learnable-sample-selection-for-monocular-3d-detection/ 博客的简述]]> Iter3DDet: Depth Guided Iterative Fusion and Refinement for Monocular 3D Object Detection https://zhaoylee.github.io/Blogs_lovelt/posts/iter3ddet---depth-guided-iterative-fusion-and-refinement-for--monocular-3d-object-detection/ Sat, 04 Apr 2026 12:24:33 +0800 zhaoylee https://zhaoylee.github.io/Blogs_lovelt/posts/iter3ddet---depth-guided-iterative-fusion-and-refinement-for--monocular-3d-object-detection/ 博客的简述]]> Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection https://zhaoylee.github.io/Blogs_lovelt/posts/mix-teaching--a-simple-unified-and-effective--semi-supervised-learning-framework-for--monocular-3d-object-detection/ Mon, 30 Mar 2026 23:17:02 +0800 zhaoylee https://zhaoylee.github.io/Blogs_lovelt/posts/mix-teaching--a-simple-unified-and-effective--semi-supervised-learning-framework-for--monocular-3d-object-detection/ 本文提出 Mix-Teaching，首个专为单目 3D 目标检测设计的半监督学习统一框架。针对伪标签“低精度与低召回率”导致的确认偏差痛点，创新性提出“分解-重组”的跨帧实例级混合机制，并结合基于不确定性的过滤策略，优雅且高效地释放了无标注数据的潜力。]]> Adaptive Dual Uncertainty Optimization: Boosting Monocular 3D Object Detection under Test Time Shifts https://zhaoylee.github.io/Blogs_lovelt/posts/adaptive-dual-uncertainty-optimization---boosting-monocular-3d-object-detection-under-test-time-shifts/ Mon, 30 Mar 2026 10:44:00 +0800 zhaoylee https://zhaoylee.github.io/Blogs_lovelt/posts/adaptive-dual-uncertainty-optimization---boosting-monocular-3d-object-detection-under-test-time-shifts/ 针对 M3OD 在未知域“测试时偏移”导致的性能断崖，本文提出双重不确定性优化 (DUO)。核心是通过无监督 Focal Loss 压制语义模糊，并用语义感知法向量约束修复空间几何坍塌，大幅提升落地鲁棒性。]]> OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection https://zhaoylee.github.io/Blogs_lovelt/posts/obmo-one-bounding-box-multiple-objects-for-monocular-3d-object-detection/ Tue, 24 Mar 2026 10:48:33 +0800 zhaoylee https://zhaoylee.github.io/Blogs_lovelt/posts/obmo-one-bounding-box-multiple-objects-for-monocular-3d-object-detection/ 简述:一个框对应多个物体位置，通过施加软标签，帮助网络训练稳定，从而提升一定的性能;]]> OCM3D: Object-Centric Monocular 3D Object Detection https://zhaoylee.github.io/Blogs_lovelt/posts/ocm3d--object-centric-monocular-3d-object-detection/ Mon, 16 Mar 2026 09:12:18 +0800 zhaoylee https://zhaoylee.github.io/Blogs_lovelt/posts/ocm3d--object-centric-monocular-3d-object-detection/

🏛️ 会议/期刊：arxiv
📅 发表年份：2021
💻 开源代码：OBMO_GUPNet
📄 论文题目：OCM3D: Object-Centric Monocular 3D Object Detection

1. 文献背景、研究目的与核心问题

  • 研究背景：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。

    ]]> LR3D: Improving Distant 3D Object Detection Using 2D Box Supervision https://zhaoylee.github.io/Blogs_lovelt/posts/lr3d--improving-distant-3d-object-detection-using-2d-box-supervision/ Sun, 15 Mar 2026 22:23:00 +0800 zhaoylee https://zhaoylee.github.io/Blogs_lovelt/posts/lr3d--improving-distant-3d-object-detection-using-2d-box-supervision/

    🏛️ 会议/期刊：CVPR
    📅 发表年份：2024
    💻 开源代码：无
    📄 论文题目：Improving Distant 3D Object Detection Using 2D Box Supervision

    这篇由 NVIDIA 等机构的研究人员发表在 CVPR 2024 的重磅论文 《Improving Distant 3D Object Detection Using 2D Box Supervision》(简称 LR3D)，切入了一个目前高阶自动驾驶极其头疼的落地难题：远距离感知（Long-Range Detection）。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。

    ]]>         StreamPETR-QAF2D：Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors https://zhaoylee.github.io/Blogs_lovelt/posts/streampetr-qaf2d--enhancing-3d-object-detection-with-2d-detection-guided-query-anchors/ Sun, 15 Mar 2026 21:59:16 +0800 zhaoylee https://zhaoylee.github.io/Blogs_lovelt/posts/streampetr-qaf2d--enhancing-3d-object-detection-with-2d-detection-guided-query-anchors/

    🏛️ 会议/期刊：CVPR
    📅 发表年份：2024
    💻 开源代码：nullmax-vision/QAF2D-CVPR 2024
    📄 论文题目：Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors

    这篇发表于 CVPR 2024 的论文 《Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors》(简称 QAF2D) 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”。

    ]]>         OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection https://zhaoylee.github.io/Blogs_lovelt/posts/obmo--one-bounding-box-multiple-objects-for-monocular-3d-object-detection/ Sun, 15 Mar 2026 21:59:12 +0800 zhaoylee https://zhaoylee.github.io/Blogs_lovelt/posts/obmo--one-bounding-box-multiple-objects-for-monocular-3d-object-detection/

    🏛️ 会议/期刊：IEEE TIP
    📅 发表年份：2023
    💻 开源代码：mrsempress/OBMO_patchnet
    📄 论文题目：OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection

    这篇发表于 IEEE TIP (2023) 的经典论文 《OBMO: One Bounding Box Multiple Objects for Monocular 3D Object Detection》 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。

    ]]>         Open Vocabulary Monocular 3D Object Detection https://zhaoylee.github.io/Blogs_lovelt/posts/open-vocabulary-monocular-3d-object-detection/ Sun, 15 Mar 2026 21:14:37 +0800 zhaoylee https://zhaoylee.github.io/Blogs_lovelt/posts/open-vocabulary-monocular-3d-object-detection/

    🏛️ 会议/期刊：3DV
    📅 发表年份：2026
    💻 开源代码：UVA-Computer-Vision-Lab/ovmono3d
    📄 论文题目：Open Vocabulary Monocular 3D Object Detection

    一、 背景、研究目的与核心问题

    • 研究背景： 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）。

      ]]>