Learning-Deep-Learning

Source of papers

Researchers’ webpage

• Kaiming He [FAIR]
• Raquel Urtasun [Uber ATG]
• Hao Su [UCSD]
• Charles R. Qi [Stanford, Waymo]
• Daniel Cremers [TUM]
• Andreas Geiger [Univ of Tübingen, MPI]
• Sudeep Pillai [Toyota]
• Laura Leal-Taixe [TUM]
• Bat El Shlomo [GM Israel]

Zhihu

• 黄飘
• 黄浴

Best papers

• CPVR 2019
• ICCV 2019
• CVPR 2020

Others

• Arxiv sanity
• A first glimpse into Autonomous driving’s technical stack

Github repos

• MMAction2 [268 stars]
• Kalman and Bayesian Fitlers [8.7k stars] ipynb book
• simple-faster-rcnn-pytorch (2.1k stars) [Notes]
• YOLACT/YOLACT++ [2.1k stars]
• Yolov3 ultralytic [4.7k stars]
• MonoLoco [131 stars]
• A Baseline for 3D Multi-Object Tracking [548 stars]
• ROLO: recurrent YOLO
• point rend
• Carla data export
• openpilot
• 3D Lane Dataset
• MicroGrad
• OpenVSLAM (2.3k stars)
• ORB SLAM2 and Docker version
• PySLAM v2

Youtube channels

• Modern C++ for computer vision
• SLAM by Cyrill Stachniss
• Understanding Sensor Fusion and Tracking by Matlab
• Understanding Kalman Filters by Matlab

Talks

• Andrej Karpathy’s Talks
• Drago Anguelov: Scale AI’s TransformX Presentation

What autonomous driving companies do

• Zoox