Virtual Machine for the Web

A cat(1) clone with wings.

A simple, fast and user-friendly alternative to 'find'

Conveniently add film grain, seamless texture overlays, and manipulate pixels to achieve nostalgic and artistic effects in p5.js sketches and artworks.

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

[IEEE ICRA'23] A new lightweight LiDAR-inertial odometry algorithm with a novel coarse-to-fine approach in constructing continuous-time trajectories for precise motion correction.

ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM

Mirror of the Basalt repository. All pull requests and issues should be sent to https://gitlab.com/VladyslavUsenko/basalt

A Robust and Versatile Monocular Visual-Inertial State Estimator

Implementation of my article "A Robust Statistics Approach for Plane Detection in Unorganized Point Clouds"

A LC3 virtual machine implementation in a few lines of C code

Create beautiful diagrams just by typing notation in plain text.

Simple didactic code + tutorial for the Walk on Stars (WoSt) algorithm

Code for "Detector-Free Structure from Motion", CVPR 2024

It's React, but in Python

Image inpainting tool powered by SOTA AI Model. Remove any unwanted object, defect, people from your pictures or erase and replace(powered by stable diffusion) any thing on your pictures.

🎬 super easy recording for p5.js animations

Copy/paste detector for programming source code.

A pure-functional implementation of a machine learning transformer model in Python/JAX

A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

The state-of-the-art image restoration model without nonlinear activation functions.

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

Publication-ready NN-architecture schematics.

Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"

Generating Training Data Made Easy

Model summary in PyTorch similar to `model.summary()` in Keras

a simple node.js library using Google Fast Fourier Color Constancy (ffcc)

My conference talks