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Open nowPosted 16 days ago

双目深度算法实习生

mammotion/campus_recruitment12 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now双目深度算法实习生mammotion/campus_recruitment · Shenzhen, Guangdong, Chinese Mainland
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  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. 设计双目深度网络,面向智能庭院机器人场景,设计轻量化双目深度估计网络。针对相机视角低的特点,研究并利用地面几何先验(Ground Prior),在保证精度的前提下提升计算效率,满足嵌入式平台的部署要求。 2. 优化边缘与细节,针对台阶边缘、物体轮廓等复杂区域,优化深度估计的锐度与一致性,重点解决深度点云在边缘处的拖影(Flying Pixels)和遮挡区域预测不准的问题。 3. 参与模型训练与调优,基于海量庭院场景双目数据,进行模型训练、Loss设计与调优,协助排查训练不收敛问题,提升模型在非结构化场景下的泛化能力。 4. 参与数据与评测闭环:参与双目深度数据集的构建与清洗,设计合理的评测指标(如边缘误差、点云平滑度等),协助建立从数据采集到模型迭代的闭环流程。 5. 总结研究成果,鼓励投稿到CVPR/ICCV/CoRL/RAL/TRO等会议或期刊。

1. 背景:计算机视觉、机器人、自动化或相关方向研究生/博士生在读,能保证至少3个月实习时间。 2. 基础:具备扎实的深度学习理论基础,熟悉双目立体匹配或单目深度估计的基本原理。了解经典的深度估计网络(如PSMNet, RAFT-Stereo, STTR, DepthAnything等)及其优缺点。了解相机模型、极线几何(Epipolar Geometry)及立体校正(Rectification)等基础概念。 3. 硬能力:熟练使用PyTorch,有实际的模型训练、调试或复现经验。熟练使用Python/C++其中一种编码语言,具备良好的工程实现能力。具备较强的问题拆解能力,能独立设计实验验证想法(如针对地面先验设计消融实验) 4. 软素质:良好的团队协作与沟通能力,乐于分享技术见解,有钻研精神。 5. 加分项:有自动驾驶/机器人深度估计相关项目经验。熟悉点云处理(如Open3D, PCL),了解深度图转点云后的滤波与平滑处理。有顶会论文发表或开源项目贡献。

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