双愚 (HuangCongQing)

HuangCongQing

User data from Github https://github.com/HuangCongQing

Company:UCAS Master

Location:Beijing, China

Home Page:https://www.linkedin.com/in/chongqing-huang-730228149/

GitHub:@HuangCongQing

双愚's repositories

pcl-learning

🔥PCL(Point Cloud Library)点云库学习记录

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UCAS_Course_2019

**科学院大学2019-2020课程(秋季,春季,夏季)

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ROS

🔥ROS(c++)机器人操作系统 学习(写于2020年夏)

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3D-Point-Clouds

🔥3D点云目标检测&语义分割(深度学习)-SOTA方法,代码,论文,数据集等

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3D-LIDAR-Multi-Object-Tracking

🔥3D-MOT(点云多目标检测和追踪C++) (2020 · 秋) 代码有详细注解

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awesome-data-labeling-tools

图像images/点云point clouds标注工具汇总

plane_fit_ground_filter

点云分割论文2017 Fast segmentation of 3d point clouds: A paradigm on lidar data for autonomous vehicle applications

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Point-Clouds-Visualization

visualization点云可视化(open3D, mayavi, rviz(ros), PCL等)

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linefit_ground_segmentation_details

快速3D点云分割论文代码(带注解):Fast segmentation of 3d point clouds for ground vehicles

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pcdet-note

OpenPCDet 代码重点注解笔记

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college-resource

各大高校大学生课程学习资源

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mmdetection3d-note

mmdetection3d 代码重点注解笔记

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cuda-learning

cuda编程学习入门

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open3d-learning

🔥 open3d Point cloud processing and visualization(点云处理和可视化)

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Python

Python学习

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model-compression-optimization

model compression and optimization for deployment for Pytorch, including knowledge distillation, quantization and pruning.(知识蒸馏,量化,剪枝)

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PointPainting-notes

PointPainting 笔记(代码已fix,可正常运行)

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download_3D_dataset

三维点云数据集下载sh脚本(目标检测,语义分割, ...)

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Spider

爬虫python3 (request,BeautifulSoup,xpath,re,Selenium,wordcloud等模块)

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python-libraries

python库(numpy,pandas...)学习

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Transformer

Transformer学习记录

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Django-backend

Django web服务端(后端)开发学习

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Embodied-AI

具身智能技术栈学习路线和相关资料

License:MITStargazers:1Issues:1Issues:0

go-learning

go语言学习 适合分布式开发,高并发,微服务

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LiDARWeather

[ECCV 2024] Official code of "Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse Weather".

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vue-element-blink

博灵(Blink)科技Web

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mmdetection3d

OpenMMLab's next-generation platform for general 3D object detection.

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SemanticSTF

(CVPR 2023) The official project of "3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds"

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