Flux3D.jl:Julia中的3D计算机视觉库

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Flux3D.jl Flux3D.jl是3D视觉库,完全用Julia编写。 该软件包利用和作为构建3D视觉模型和支持差异化的基础。 该程序包还支持对CUDA GPU的加速。此库的主要目的是提供: 用于存储和计算的3D数据(如PointCloud,TriMesh和VoxelGrid)的批处理数据结构。 用于处理3D结构的变换和通用实用程序。 用于定义损失目标和预定义3D模型的度量。 轻松访问加载和预处理标准3D数据集。 PointCloud,TriMesh和VoxelGrid的可视化实用程序。 不同3D结构之间的相互转换。 任何建议,问题和请求都将受到欢迎。 安装 该软件包足够稳
Flux3D_jl-master.zip
内容介绍
<p align="center"> </p> <p> <h1 align="center">Flux3D.jl</h1> </p> <p align="center"> <a href="https://fluxml.ai/Flux3D.jl/dev" alt="Dev" rel='nofollow' onclick='return false;'> <img src="https://img.shields.io/badge/docs-dev-blue.svg"/> </a> <a href="https://fluxml.ai/Flux3D.jl/stable" alt="Dev" rel='nofollow' onclick='return false;'> <img src="https://img.shields.io/badge/docs-stable-blue.svg"/> </a> <a href="https://github.com/FluxML/Flux3D.jl/actions" alt="Build Status" rel='nofollow' onclick='return false;'> <img src="https://github.com/FluxML/Flux3D.jl/workflows/CI/badge.svg"/> </a> <a href="https://gitlab.com/JuliaGPU/Flux3D-jl/commits/master" alt="Pipeline Status" rel='nofollow' onclick='return false;'> <img src="https://gitlab.com/JuliaGPU/Flux3D-jl/badges/master/pipeline.svg"/> </a> <a href="https://codecov.io/gh/FluxML/Flux3D.jl" alt="Codecov" rel='nofollow' onclick='return false;'> <img src="https://codecov.io/gh/FluxML/Flux3D.jl/branch/master/graph/badge.svg?token=8kpPqDfChf"/> </a> <a href="https://github.com/SciML/ColPrac" alt="ColPrac: Contributor's Guide on Collaborative Practices for Community Packages" rel='nofollow' onclick='return false;'> <img src="https://img.shields.io/badge/ColPrac-Contributor's%20Guide-blueviole"/> </a> </p> <br/> Flux3D.jl is a 3D vision library, written completely in Julia. This package utilizes [Flux.jl](https://github.com/FluxML/Flux.jl) and [Zygote.jl](https://github.com/FluxML/Zygote.jl) as its building blocks for training 3D vision models and for supporting differentiation. This package also have support of CUDA GPU acceleration with [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl).The primary motivation for this library is to provide: * Batched Data structure for 3D data like PointCloud, TriMesh and VoxelGrid for storing and computation. * Transforms and general utilities for processing 3D structures. * Metrics for defining loss objectives and predefined 3D models. * Easy access to loading and pre-processing standard 3D datasets. * Visualization utilities for PointCloud, TriMesh and VoxelGrid. * Inter-Conversion between different 3D structures. Any suggestions, issues and pull requests are most welcome. <p align="center"> </p> ## Installation This package is stable enough for use in 3D Machine Learning Research. It has been registered. To install the latest release, type the following in the Julia 1.3+ prompt. ```julia julia> ] (v1.3) pkg> add Flux3D ``` To install the master branch type the following ```julia julia> ] (v1.3) pkg> add Flux3D#master ``` ## Examples <div align="center"> <table> <tr> <th style="text-align:center"> <a href="https://fluxml.ai/Flux3D.jl/dev/tutorials/pointnet" rel='nofollow' onclick='return false;'>PointNet Classfication</a> </th> <th style="text-align:center"> <a href="https://fluxml.ai/Flux3D.jl/dev/tutorials/dgcnn" rel='nofollow' onclick='return false;'>DGCNN Classification</a> </th> <th style="text-align:center"> <a href="https://fluxml.ai/Flux3D.jl/dev/tutorials/fit_mesh" rel='nofollow' onclick='return false;'>Supervised 3D reconstruction</a> </th> </tr> <tr> <td align="center"> <a href="https://fluxml.ai/Flux3D.jl/dev/tutorials/pointnet" rel='nofollow' onclick='return false;'> </a> </td> <td align="center"> <a href="https://fluxml.ai/Flux3D.jl/dev/tutorials/dgcnn" rel='nofollow' onclick='return false;'> </a> </td> <td align="center"> <a href="https://fluxml.ai/Flux3D.jl/dev/tutorials/fit_mesh" rel='nofollow' onclick='return false;'> </a> </td> </tr> </table> </div> ## Usage Examples ```julia julia> using Flux3D julia> m = load_trimesh("teapot.obj") |> gpu TriMesh{Float32, UInt32, CUDA.CuArray} Structure: Batch size: 1 Max verts: 1202 Max faces: 2256 offset: -1 Storage type: CUDA.CuArray julia> laplacian_loss(m) 0.05888283f0 julia> compute_verts_normals_packed(m) 3×1202 CUDA.CuArray{Float32,2,Nothing}: 0.00974202 0.00940375 0.0171322 … 0.841262 0.777704 0.812894 -0.999953 -0.999953 -0.999848 -0.508064 -0.607522 -0.557358 6.14616f-6 0.00249814 -0.00317568 -0.184795 -0.161533 -0.168985 julia> new_m = Flux3D.normalize(m) TriMesh{Float32, UInt32, CUDA.CuArray} Structure: Batch size: 1 Max verts: 1202 Max faces: 2256 offset: -1 Storage type: CUDA.CuArray julia> save_trimesh("normalized_teapot.obj", new_m) ``` ## Citation If you use this software as a part of your research or teaching, please cite this GitHub repository. For convenience, we have also provided the BibTeX entry in the form of `CITATION.bib` file. ``` @misc{Suthar2020, author = {Nirmal Suthar, Avik Pal, Dhairya Gandhi}, title = {Flux3D: A Framework for 3D Deep Learning in Julia}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/FluxML/Flux3D.jl}}, } ``` ## Benchmarks ### PointCloud Transforms (Flux3D.jl and Kaolin) ![Benchmark plot for PointCloud transforms](docs/src/assets/bm_pcloud.png) ### TriMesh Transforms (Flux3D.jl and Kaolin) ![Benchmark plot for TriMesh transforms](docs/src/assets/bm_trimesh.png) ### Metrics (Flux3D.jl and Kaolin) ![Benchmark plot for Metrics](docs/src/assets/bm_metrics.png) ## Current Roadmap - [X] Add Batched Structure for PointCloud and TriMesh. - [X] Add Transforms/Metrics for PointCloud and TriMesh. - [X] GPU Support using CUDA.jl - [X] Add Dataset support for ModelNet10/40. - [X] Add Batched 3D structure and Transform for Voxels. - [X] Interconversion between different 3D structures like PointCloud, Voxel and TriMesh. - [ ] Add more metrics for TriMesh (like normal_consistency and cloud_mesh_distance)
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