Mycat2

This document provides an overview of Mycat2, a distributed relational database middleware, and Mitsuba 3, a research-oriented rendering system. Both projects offer comprehensive features and are actively developed, though Mitsuba 3's master branch is currently undergoing significant, undocumented changes. Mycat2 utilizes Java and supports various backend databases, while Mitsuba 3 leverages Python and offers high-performance rendering capabilities with GPU acceleration.

Mycat2

Distributed database based on MySQL or JDBC.

官网

http://mycatone.top/

Software introduction

Mycat2 is a distributed relational database (middleware) developed by the Mycat community. It supports distributed SQL queries, is compatible with the MySQL communication protocol, supports a variety of back-end databases with Java ecology, and improves data query processing capabilities through data fragmentation.

软件介绍

Mycat2是Mycat社区开发的一款分布式关系型数据库(中间件)。它支持分布式SQL查询，兼容MySQL通信协议，以Java生态支持多种后端数据库，通过数据分片提高数据查询处理能力。

gitee:https://gitee.com/MycatOne/Mycat2

github:https://github.com/MyCATApache/Mycat2

v1.22-release

doc-en

doc-cn

Features

Framework

Currently, only mycat2 is supported on java8, and will be supported in other versions later.

License

Apache LICENSE

from 2023.8

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}