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100-days-of-ml-code

100 Days of ML Coding

3d-machine-learning

A resource repository for 3D machine learning

3dfeatnet

3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration

3dssd

3DSSD: Point-based 3D Single Stage Object Detector (CVPR 2020)

3dv_tutorial

An Invitation to 3D Vision: A Tutorial for Everyone

996.icu

Repo for counting stars and contributing. Press F to pay respect to glorious developers.

ai-edu

AI education materials for Chinese students, teachers and IT professionals.

apollo

An open autonomous driving platform

apollo-kernel

Collections of Apollo Kernels

apollo-platform

Collections of Apollo Platform Software

autoware

Open-Source To Self-Driving.

awesome-lane-detection

A paper list of lane detection.

awesome-object-detection

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

awesome-point-cloud-analysis

A list of papers and datasets about point cloud analysis (processing)

bevfusion

BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

cartographer

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

code-for-lucas-kanade-20-years-on

colmap

COLMAP - Structure-from-Motion and Multi-View Stereo

computer-vision-tutorial

computer vision tutorial guide courses books codes slides resources

cpi

Closed-form Preintegration for Graph-based Visual-Inertial Navigation

cross_view_transformers

Cross-view Transformers for real-time Map-view Semantic Segmentation (CVPR 2022 Oral)

csapp2e

ctrip

cv-arxiv-daily

分享计算机视觉每天的arXiv文章

cvpr2019

cvpr2019 papers，极市团队整理

cvpr2019-code

CVPR 2019 Paper with Code

dare

Density Adaptive Point Set Registration

darknet

YOLOv4 - Neural Networks for Object Detection (Windows and Linux version of Darknet )

deepbayes-2018

Seminars DeepBayes Summer School 2018

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06