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Open nowPosted 16 days ago

多模态融合感知实习生

mammotion/campus_recruitment12 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now多模态融合感知实习生mammotion/campus_recruitment · Shenzhen, Guangdong, Chinese Mainland
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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. 57.8%60 days
This job: posted 16 days ago

The posting

1. 算法调研与评测:调研激光雷达与环视相机多模态融合的Occ网络前沿方案,在庭院场景数据集上进行复现与性能评测。 2. 多模态架构设计:针对庭院场景(如非结构化地形、近距障碍物等),探索视觉与雷达点云的多模态融合方案。重点研究利用雷达提供绝对尺度、视觉提供稠密语义的深度补全/尺度恢复策略,设计适合庭院的Occ网络架构。 3. 参与数据闭环,协助团队梳理多模态数据采集需求(如传感器标定、同步等)。参与自动标注脚本的设计与验证,协助测试团队落地数据采集与标注流程。 4. 参与多模态Occ模型的训练与调优工作,协助排查训练不收敛问题,在庭院数据集上协助提升模型精度与泛化能力。 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、机器学习或相关方向研究生/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BEV/Occ基本概念。了解多模态融合的基本原理,熟悉至少一种经典的多模态3D检测/Occ方法(如BEVFusion, MetaBEV, OccFusion等)。了解激光雷达点云处理基础(如PointPillars, VoxelNet等)或视觉深度估计。 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。具备较强的问题拆解能力,能独立设计实验验证想法。 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/机器人多模态感知相关项目经验,或熟悉传感器标定、时间同步等底层数据处理;或有顶会论文发表或开源项目贡献。

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