小米感知算法实习生
实习兼职地点:上海状态:招聘
任职要求
1. 具备一定的科研背景:在自动驾驶相关的感知算法(包括BEV感知,Lidar 3D Detection/Segmentation,Occupancy Network,EndToEnd, 多传感器融合,NerF,单目/多目深度估计,三维重建,LMM, AIGC大模型)中的一个或多个领域有过深入研究的经历; 2. 熟悉一个或多个深度学习框架,有tensorflow/pytorch的深度使用经验者优先; 3. 有过相关领域的会议论文发表(CVPR、ICCV、ECCV等等)或在相关领域的学术竞赛(ImageNet、COCO、Kitti、Waymo等等)中取得较好成绩者优先; 4. 相关领域有国内知名实验室、企业实习经历或发表相关论文、有算法编程竞赛/自动驾驶或机器人相关竞赛经历:如KITTI、nuScenes、Waymo、中国智能车未来挑战赛等优先; 5. 面对未知的领域和问题,有非常强的自学能力。
工作职责
1. 负责自动驾驶业务场景下的感知算法研发,调研跟进前沿算法,辅助业务落地,包括但不限于: a)激光雷达3D多任务感知,如3D Object Detection,3D Semantic Segmentation, 3D Occupancy Flow Prediction, EndToEnd, World Model等; b)BEV视觉感知和前融合算法,如BEVDet,BEVFusion,Occupancy Network等; c)感知全链路研发,如跟踪,多传感器融合等; 2. 负责自动驾驶数据算法研发,助力数据自动化生产和4D真值构建,包括单不限于: a) SLAM/SFM算法研发; b) 基于Deep Learning的MonoDepth/Multi-View Stereo算法研发; c)NerF相关算法研发; d)大模型AIGC、LMM、NerF、3D Gaussian生成等技术研发; 3. 将相关结果总结沉淀,发表高水平论文 。
包括英文材料
自动驾驶+
https://www.youtube.com/watch?v=_q4WUxgwDeg&list=PL05umP7R6ij321zzKXK6XCQXAaaYjQbzr
Lecture: Self-Driving Cars (Prof. Andreas Geiger, University of Tübingen)
https://www.youtube.com/watch?v=NkI9ia2cLhc&list=PLB0Tybl0UNfYoJE7ZwsBQoDIG4YN9ptyY
You will learn to make a self-driving car simulation by implementing every component one by one. I will teach you how to implement the car driving mechanics, how to define the environment, how to simulate some sensors, how to detect collisions and how to make the car control itself using a neural network.
算法+
https://roadmap.sh/datastructures-and-algorithms
Step by step guide to learn Data Structures and Algorithms in 2025
https://www.hellointerview.com/learn/code
A visual guide to the most important patterns and approaches for the coding interview.
https://www.w3schools.com/dsa/
大模型+
https://www.youtube.com/watch?v=xZDB1naRUlk
You will build projects with LLMs that will enable you to create dynamic interfaces, interact with vast amounts of text data, and even empower LLMs with the capability to browse the internet for research papers.
https://www.youtube.com/watch?v=zjkBMFhNj_g
深度学习+
https://d2l.ai/
Interactive deep learning book with code, math, and discussions.
TensorFlow+
https://www.youtube.com/watch?v=tpCFfeUEGs8
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
https://www.youtube.com/watch?v=ZUKz4125WNI
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
PyTorch+
https://datawhalechina.github.io/thorough-pytorch/
PyTorch是利用深度学习进行数据科学研究的重要工具,在灵活性、可读性和性能上都具备相当的优势,近年来已成为学术界实现深度学习算法最常用的框架。
https://www.youtube.com/watch?v=V_xro1bcAuA
Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python.
CVPR+
https://cvpr.thecvf.com/
ICCV+
https://iccv.thecvf.com/
ICCV is the premier international computer vision event comprising the main conference and several co-located workshops and tutorials.
ECCV+
https://eccv.ecva.net/
ECCV is the official event under the European Computer Vision Association and is biannual on even numbered years.
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