Cong Jie's starred repositories
graph-cut-ransac
The Graph-Cut RANSAC algorithm proposed in paper: Daniel Barath and Jiri Matas; Graph-Cut RANSAC, Conference on Computer Vision and Pattern Recognition, 2018. It is available at http://openaccess.thecvf.com/content_cvpr_2018/papers/Barath_Graph-Cut_RANSAC_CVPR_2018_paper.pdf
pytorch-PPUU
Code for Prediction and Planning Under Uncertainty (PPUU)
slambook-en
The English version of 14 lectures on visual SLAM.
VINS-Fusion
An optimization-based multi-sensor state estimator
tonav-supplementary-files
This is repository with supplementary files for my master's thesis.
iTerm2-Color-Schemes
Over 250 terminal color schemes/themes for iTerm/iTerm2. Includes ports to Terminal, Konsole, PuTTY, Xresources, XRDB, Remmina, Termite, XFCE, Tilda, FreeBSD VT, Terminator, Kitty, MobaXterm, LXTerminal, Microsoft's Windows Terminal, Visual Studio, Alacritty
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
3d-vehicle-tracking
Official implementation of Joint Monocular 3D Vehicle Detection and Tracking (ICCV 2019)
BezierInfo-2
The development repo for the Primer on Bézier curves, https://pomax.github.io/bezierinfo
imagenet18_old
Code to reproduce "imagenet in 18 minutes" DAWN-benchmark entry
online_photometric_calibration
Implementation of online photometric calibration (https://vision.in.tum.de/research/vslam/photometric-calibration)
semantic-segmentation-editor
Web labeling tool for bitmap images and point clouds
liblanelet
Lanelet maps have been introduced in the context of the autonomous completion of the Bertha-Benz-Memorial-Route in 2013
albumentations
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
pytorch-summary
Model summary in PyTorch similar to `model.summary()` in Keras
spinningup
An educational resource to help anyone learn deep reinforcement learning.