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Open nowPosted 129 days ago

【27届】3D重建算法工程师

x2-robot/91213080 open roles

Where
Shenzhen, Guangdong, Chinese Mainland
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Your applicationOpen now【27届】3D重建算法工程师x2-robot/912130 · Shenzhen, Guangdong, Chinese Mainland

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The clock on this job

Early applications get read.

12.1% 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. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 129 days ago

The posting

1. 前馈式重建算法研发：负责研发基于 Transformer 先验或多视角交叉注意力机制的前馈式重建算法， 多视图几何与神经网络架构设计： 2. 负责设计高效的跨视角几何对齐、像素级对齐（Pixel-aligned）特征预测网络以及极线约束（Epipolar Geometry）机制，攻克前馈网络在未知物体与复杂场景上的泛化性（Generalization）难题。 3. 传统与生成式 3DGS 重建优化：深入优化 3D Gaussian Splatting (3DGS) 场景重建管线，针对特定工业或机器人场景，改进高斯参数初始化、致密化（Densification）策略，提升在复杂光照和弱纹理区域的重建精度。 4. 高效渲染管线与算子优化：负责 3DGS 前向与后向光栅化（Rasterization）渲染管线的底层深度定制与优化，编写高并发的 CUDA Kernel / Triton 算子，提升前馈预测与动态渲染的综合吞吐率。 5. 数据生成与 3D 资产管线构建：搭建并优化大规模多视角/3D 互动数据集（如 Objaverse, ScanNet, MVImgNet 等）的清洗与自动化处理管线，利用 Blender 或物理仿真器构建高质合成数据生成系统。

1. 毕业时间介于2026年9月至2027年8月的国内外高校的本科生、硕士生、博士生，计算机、自动化、数字媒体技术、应用数学等相关专业优先。 2. 精通 Python 和 C++，熟练掌握 PyTorch 深度学习框架，具备优秀的面向对象设计与大型分布式训练（DDP/DeepSpeed）工程能力。 核心技术背景： 3. 深入理解多视图几何理论（包括相机标定、对极几何、MVS、透视投影、SfM 基础）。精通 3D Gaussian Splatting (3DGS) 的底层数学物理原理与经典开源框架，对光栅化渲染有实际修改、性能调优或重构经验。 4. 熟悉至少一种主流前馈式或生成式 3D 架构特点（如基于 Cross-Attention 的多视角特征聚合网络，或 Epipolar-guided Transformer）。 5. 熟练掌握 Linux 开发环境，熟练使用 Open3D, Trimesh, PyTorch3D 等三维数据处理库

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