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Open nowPosted 16 days ago

前馈三维重建实习生

mammotion/campus_recruitment12 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now前馈三维重建实习生mammotion/campus_recruitment · Shenzhen, Guangdong, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on mammotion/campus_recruitment's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: posted 16 days ago

The posting

1. 参与基于前馈神经网络 + Bundle Adjustment (BA) 联合优化的三维重建算法研发。利用前馈网络（如VGGT、DUSt3R、MASt3R等）实现快速深度/点云初始估计，并结合经典优化框架（如Ceres）进行精细化调整与全局一致性优化，构建高精度、高鲁棒性的三维结构输出。 2. 设计数据预处理与迭代策略：针对庭院数据特点（多视角、光照、跨季节场景变化等），协助设计鲁棒的数据预处理策略。参与挖掘高价值样本，协助迭代网络性能，降低对完美标注数据的依赖。 3. 探索前馈网络和后端优化的协同机制，如端到端可微BA、网络引导的BA初值、优化结果转换为camera token传入前馈网络等等。 4. 实验与评测：在庭院场景实现高精度的稠密三维重建。 5. 总结研究成果，鼓励投稿到CVPR/ICCV/CoRL/RAL/TRO等会议或期刊。 6. 成果沉淀（可选）：总结研究成果，有机会参与投稿CVPR/ICCV/ICRA/IROS等机器人或计算机视觉领域顶会。

1. 背景：计算机视觉、三维重建、摄影测量或相关方向硕士/博士在读，能保证至少3-6个月实习时间。 2. 基础（满足以下任一方向即可）： - 经典几何方向：扎实掌握SFM基础理论，熟悉相机模型、对极几何、三角化、BA优化、PnP等传统方法；了解Ceres Solver或类似优化库的使用。 - 深度学习方向：深入理解基于神经网络的三维重建/深度估计范式，熟悉前馈式重建代表性工作（如VGGT、DUSt3R、MASt3R、DepthAnything等）。 3. 硬能力：精通Python，熟悉PyTorch框架；了解C++，具备良好的工程实现能力与代码习惯（有C++/Ceres实际项目经验者优先）。 4. 软素质：具备优秀的跨团队协作能力，能够与感知、定位团队紧密配合，推动三维重建技术在实际场景中的落地。 5. 加分项： - 有CVPR/ICCV/T-PAMI/T-RO等CV或机器人相关顶会/顶刊发表经历者优先； - 有自动驾驶/机器人三维重建、SLAM或深度估计相关项目经验； - 有开源项目贡献或高质量个人技术博客。

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