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Open nowPosted 111 days ago

三维重建算法实习生

arashivision/campus358 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now三维重建算法实习生arashivision/campus · Shenzhen, Guangdong, Chinese Mainland

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

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The clock on this job

Early applications get read.

11.5% 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. 4.4%3 days
  3. 11.5%7 days
  4. 18.1%14 days
  5. 35.6%30 days
  6. 44.6%60 days
This job: posted 111 days ago

The posting

1. 大规模场景几何重建：研发基于多视图几何（MVS）与深度估计的重建算法，解决大规模户外场景下的尺度漂移、遮挡恢复及几何拓扑提取难题，实现从非结构化影像到高精度几何实体的转化。 2. 神经表征与渲染算法优化：深入研究并改进 3D/2D Gaussian Splatting (GS) 或 其他生成式算法，针对具身智能仿真需求，优化大规模场景的显存管理、分块策略及长序列渲染的几何一致性。 3. 算法管线工程化：设计并实现高性能、可扩展的三维重建算法流水线；负责重建系统中的核心算子优化（如 CUDA 核函数开发），提升大规模数据的吞吐量与处理效率。 4. 技术指标与评估体系：建立针对大规模重建的端到端评测方案，包括几何精度、法线质量及光影准确性，通过数据驱动的方式持续迭代算法性能，支撑闭环仿真训练。

1. 扎实的算法基础：计算机视觉、机器人或数学相关专业；精通多视图几何（MVS）、SfM、SLAM、点云处理及非线性优化。 2. 核心技术积累：在 3DGS、神经渲染或大规模三维重建领域有深入研究，具备处理大规模、弱纹理或复杂光照场景的实战经验。 3. 卓越的工程实现能力： • 精通 C++ 与 Python，具备严谨的编程习惯与系统架构设计能力； • 熟悉 PyTorch/LibTorch 框架，能够独立复现前沿论文并进行工程化改进； • 硬核加分：具备 CUDA 或 TensorRT 开发经验，能够编写高效的并行计算算子以解决渲染或计算瓶颈。 4. 具身背景优先：具有具身智能或自动驾驶企业/实验室实习经验，熟悉重建算法如何服务于感知算法训练或闭环仿真系统。 加分项： 1. 在 CVPR、ICCV、ECCV、NeurIPS、SIGGRAPH 等顶级学术会议发表过高水平论文。 2. 在 GitHub 上拥有高质量开源项目，或在三维重建、SLAM 相关国际竞赛中取得优异成绩。

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