單一登入

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 comprepresive supumentation and lattail sections. 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 tr​​usted 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 是分散式單一登入框架。只需要登入一次就可以存取所有互信的應用系統。

擁有"輕量級、分散式、跨域、Cookie+Token皆支援、Web+APP皆支援"等特性。現已開放原始碼，開箱即用。

Documentation

Communication

Features

Development

於2018年初，我在github上創建XXL-SSO專案倉庫並提交第一個commit，隨之進行系統結構設計，UI選型，互動設計…

於2018-12-05，XXL-SSO參與"2018年度最受歡迎中國開源軟體"評比，在當時已輸入的一萬多個國產開源專案中角逐，最終排名第55名。

於2019-01-23，XXL-SSO被評選上榜"2018年度新增開源軟體排行榜之國產TOP 50"評比，排名第8名。

至今，XXL-SSO已接入多家公司的線上產品線，接入場景如電商業務，O2O業務和核心中間件配置動態化等，截止2018-03-15為止，XXL-SSO已接入的公司包括不限於：

更多接取的公司，歡迎在登記地址登記，登記僅為了產品推廣。

歡迎大家的關注與使用，XXL-SSO也將擁抱變化，持續發展。

Contributing

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

歡迎參與專案貢獻！例如提交PR修復一個bug，或是新建Issue 討論新功能或是變更。

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.

產品開源免費，並將持續提供免費的社群技術支援。個人或企業內部可自由的存取和使用。

Donate

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

無論金額多少都足夠表達您這份心意，非常感謝：） 前往捐贈

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

或 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

或 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.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 Cresam and Vinyi, and Tizian Zeltner and Baptiste Nicolet and Miguel Creslem 和 Vinle, Znote = {https://mitsuba-renderer.org},version = {3.1.1},year = 2022}