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Open nowPosted 16 days ago

视觉感知实习生

mammotion/campus_recruitment12 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now视觉感知实习生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. 面向智能庭院机器人场景，设计视觉BEV/Occ感知系统。涵盖BEV/Occ特征构建、下游目标检测头，下游边界检测头等关键模块。训练BEV/Occ网络，与团队配合，实现训练收敛。 2. 前沿探索（选做，根据个人兴趣，重点突破以下1-2个方向） a.探索稀疏BEV网络架构(如PointBEV)。针对感受域内大部分区域是道路/草地的特点，利用稀疏采样方式重点学习有障碍物的区域。从而保持感知精度同时大幅提升计算效率。 b.探索2.5D Occ的地图表征：压缩柱体的信息，同时尽可能保留不同高度上的特征信息，如构建变分辨率的栅格、或矢量化表示地图元素。从而保留和地面机器人导航相关的空间信息，并大幅提升计算效率。 c.探索只利用2D卷积算子（部署友好）的Occ模型架构，在pytorch平台达到或接近基于3D卷积算子的感知效果。 d. 探索自监督的Occ网络训练方法，尝试新的数据闭环与训练方式。 e. 探索时序融合的Occ架构，进一步提升多视角一致性。 3. 在大规模庭院数据下，评测感知精度和计算效率。 4. 总结研究成果，鼓励投稿到CVPR/ICCV/CoRL/RAL/TRO等会议或期刊。

1. 计算机视觉、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. 具备扎实的深度学习理论基础，熟悉BEV/Occ的基本概念，了解至少一种经典方法（如LSS, BEVDepth, SparseOcc等）。 3. 熟悉至少一种深度学习框架（如PyTorch），有实际的模型训练、调优或复现经验。具备较强的问题拆解能力，能设计合理的验证步骤定位问题。 4. 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/机器人感知相关项目经验。有顶会论文发表或开源项目贡献。

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