单点登录

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 是一个分布式单点登录框架。只需要登录一次就可以访问所有相互信任的应用系统。

拥有"轻量级、分布式、跨域、Cookie+Token均支持、Web+APP均支持"等特性。现已开放源代码，开箱即用。

Documentation

Communication

Features

Development

于2018年初，我在github上创建XXL-SSO项目仓库并提交第一个commit，随之进行系统结构设计，UI选型，交互设计……

于2018-12-05，XXL-SSO参与"2018年度最受欢迎中国开源软件"评比，在当时已录入的一万多个国产开源项目中角逐，最终排名第55名。

于2019-01-23，XXL-SSO被评选上榜"2018年度新增开源软件排行榜之国产 TOP 50"评比，排名第8名。

至今，XXL-SSO已接入多家公司的线上产品线，接入场景如电商业务，O2O业务和核心中间件配置动态化等，截止2018-03-15为止，XXL-SSO已接入的公司包括不限于：

更多接入的公司，欢迎在 登记地址 登记，登记仅仅为了产品推广。

欢迎大家的关注和使用，XXL-SSO也将拥抱变化，持续发展。

Contributing

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

欢迎参与项目贡献！比如提交PR修复一个bug，或者新建 Issue 讨论新特性或者变更。

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.

产品开源免费，并且将持续提供免费的社区技术支持。个人或企业内部可自由的接入和使用。

Donate

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

无论金额多少都足够表达您这份心意，非常感谢 ：） 前往捐赠

example:

Mitsuba Renderer 3

Documentation	Tutorial videos	Linux	MacOS	Windows	PyPI

️

Warning

️

There currently is 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 scenescene = 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}