xxl sso

This document provides an overview of two open-source projects: XXL-SSO, a distributed single sign-on framework, and Mitsuba 3, a research-oriented rendering system. Both projects offer comprehensive documentation and support various platforms. The following sections detail their features, installation, and usage.

XXL-SSO

XXL-SSO, A Distributed Single-Sign-On Framework.

-- Home Page --

Introduction

XXL-SSO is a distributed single-sign-on framework. You only need to log in once to access all trusted application systems.

It has "lightweight, scalable, distributed, cross-domain, Web+APP support access" features.

Now, it's already open source code, real "out-of-the-box".

XXL-SSO is a distributed single sign-on framework. You only need to log in once to access all mutually trusted application systems.

It has the characteristics of "lightweight, distributed, cross-domain, supports both Cookie+Token and Web+APP". Now open source, ready to use out of the box.

Documentation

Communication

Features

Development

At the beginning of 2018, I created the XXL-SSO project warehouse on github and submitted the first commit. Then I carried out system structure design, UI selection, interaction design...

On 2018-12-05, XXL-SSO participated in the "2018 Most Popular Chinese Open Source Software" competition, competing among more than 10,000 domestic open source projects that had been entered at that time, and finally ranked 55th.

On 2019-01-23, XXL-SSO was selected into the "2018 New Open Source Software Ranking of Domestic TOP 50" ranking 8th.

So far, XXL-SSO has been connected to the online product lines of many companies. The access scenarios include e-commerce business, O2O business and dynamic core middleware configuration. As of 2018-03-15, XXL-SSO has been connected. Companies include but are not limited to:

More connected companies are welcome to register at the registration address. Registration is only for product promotion.

Everyone is welcome to pay attention and use, XXL-SSO will also embrace changes and continue to develop.

Contributing

Contributions are welcome! Open a pull request to fix a bug, or open an Issue to discuss a new feature or change.

Welcome to contribute to the project! For example, submit a PR to fix a bug, or create a new Issue to discuss new features or changes.

Copyright and License

This product is open source and free, and will continue to provide free community technical support. Individual or enterprise users are free to access and use.

The product is open source and free, and free community technical support will continue to be provided. It can be freely accessed and used by individuals or enterprises.

Donate

No matter how much the amount is enough to express your thought, thank you very much :) To donate

No matter how much the amount is, it is enough to express your feelings, thank you very much:) Go to donate

example:

Mitsuba Renderer 3

Documentation	Tutorial videos	Linux	MacOS	Windows	PyPI

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Warning

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There is currently a large amount of undocumented and unstable work going on in

the master branch. We'd highly recommend you use our

latest release

until further notice.

If you already want to try out the upcoming changes, please have a look at

this porting guide.

It should cover most of the new features and breaking changes that are coming.

Introduction

Mitsuba 3 is a research-oriented rendering system for forward and inverse light

transport simulation developed at EPFL in Switzerland.

It consists of a core library and a set of plugins that implement functionality

ranging from materials and light sources to complete rendering algorithms.

Mitsuba 3 is retargetable : this means that the underlying implementations and

data structures can transform to accomplish various different tasks. For

example, the same code can simulate both scalar (classic one-ray-at-a-time) RGB transport

or differential spectral transport on the GPU. This all builds on

Dr.Jit, a specialized just-in-time (JIT) compiler developed specifically for this project.

Main Features

• Cross-platform : Mitsuba 3 has been tested on Linux ( x86_64 ), macOS

  ( aarch64 , x8664 ), and Windows ( x8664 ).

• High performance : The underlying Dr.Jit compiler fuses rendering code

  into kernels that achieve state-of-the-art performance using

  an LLVM backend targeting the CPU and a CUDA/OptiX backend

  targeting NVIDIA GPUs with ray tracing hardware acceleration.

• Python first : Mitsuba 3 is deeply integrated with Python. Materials,

  textures, and even full rendering algorithms can be developed in Python,

  which the system JIT-compiles (and optionally differentiates) on the fly.

  This enables the experimentation needed for research in computer graphics and

  other disciplines.

• Differentiation : Mitsuba 3 is a differentiable renderer, meaning that it

  can compute derivatives of the entire simulation with respect to input

  parameters such as camera pose, geometry, BSDFs, textures, and volumes. It

  implements recent differentiable rendering algorithms developed at EPFL.

• Spectral & Polarization : Mitsuba 3 can be used as a monochromatic

  renderer, RGB-based renderer, or spectral renderer. Each variant can

  optionally account for the effects of polarization if desired.

Tutorial videos, documentation

We've recorded several YouTube videos that provide a gentle introduction

Mitsuba 3 and Dr.Jit. Beyond this you can find complete Juypter notebooks

covering a variety of applications, how-to guides, and reference documentation

on readthedocs.

Installation

We provide pre-compiled binary wheels via PyPI. Installing Mitsuba this way is as simple as running

 pip install mitsuba

on the command line. The Python package includes thirteen variants by default:

• scalar_rgb

• scalar_spectral

• scalarspectralpolarized

• llvmadrgb

• llvmadmono

• llvmadmono_polarized

• llvmadspectral

• llvmadspectral_polarized

• cudaadrgb

• cudaadmono

• cudaadmono_polarized

• cudaadspectral

• cudaadspectral_polarized

The first two perform classic one-ray-at-a-time simulation using either a RGB

or spectral color representation, while the latter two can be used for inverse

rendering on the CPU or GPU. To access additional variants, you will need to

compile a custom version of Dr.Jit using CMake. Please see the

documentation

for details on this.

Requirements

• Python >= 3.8

• (optional) For computation on the GPU: Nvidia driver >= 495.89

• (optional) For vectorized / parallel computation on the CPU: LLVM >= 11.1

Usage

Here is a simple "Hello World" example that shows how simple it is to render a

scene using Mitsuba 3 from Python:

 # Import the library using the alias "mi"import mitsuba as mi# Set the variant of the renderermi.setvariant('scalarrgb')# Load a scenes = mi.loaddict(mi.cornellbox())# Render the sceneimg = mi. render(scene)# Write the rendered image to an EXR filemi.Bitmap(img).write('cbox.exr')

Tutorials and example notebooks covering a variety of applications can be found

in the documentation.

About

This project was created by Wenzel Jakob.

Significant features and/or improvements to the code were contributed by

Sébastien Speierer,

Nicolas Roussel,

Merlin Nimier-David,

Delio Vicini,

Tizian Zeltner,

Baptiste Nicolet,

Miguel Crespo,

Vincent Leroy, and

Ziyi Zhang.

When using Mitsuba 3 in academic projects, please cite:

 @software{Mitsuba3,title = {Mitsuba 3 renderer},author = {Wenzel Jakob and Sébastien Speierer and Nicolas Roussel and Merlin Nimier-David and Delio Vicini and Tizian Zeltner and Baptiste Nicolet and Miguel Crespo and Vincent Leroy and Ziyi Zhang},note = {https://mitsuba-renderer.org},version = {3.1.1},year = 2022}