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25–48 of 68 · Most stars

A C++, header-only library for constructing JSON and JSON-like data formats, with JSON Pointer, JSON Patch, JSON Schema, JSONPath, JMESPath, CSV, MessagePack, CBOR, BSON, UBJSON, toon-format

  • json
  • cbor
  • jsonpath
  • csv

The Point Processing Toolkit (pptk) is a Python package for visualizing and processing 2-d/3-d point clouds.

An automated Themida / Code Virtualizer / WinLicense (v3.0-v4) unpacker and dump fixer tool

An Event-Driven, Asynchronous, Behavioral State Machine Library for ROS (Robotic Operating System) applications written in C++

  • state-machine
  • ros
  • ros-packages
  • navigation

This is a package for LiDARTag, described in paper: LiDARTag: A Real-Time Fiducial Tag System for Point Clouds

  • fiducial-markers
  • lidar
  • point-clouds
  • cassie

Open source Windows x64 PE packer and crypter. Compresses and encrypts executables with a custom virtual machine into a self extracting stub.

  • compression
  • packer
  • pe-packer
  • pe-compress

A simple, cross-platform utility to classify packets into flows using only the essential 4-tuple.

CloudPeek is a lightweight, cross-platform, single-header C++ point cloud viewer. It’s designed for simplicity and efficiency, requiring no heavy libraries like PCL or Open3D. Ideal for visualizing and interacting with 3D data from LiDAR, photogrammetry, or other datasets, CloudPeek delivers powerful, real-time exploration in a minimalistic package

R package for segmentation, registration and generating web-based brain atlases from microscope images

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.