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https://github.com/fluxml/flux3d.jl
3D computer vision library in Julia
https://github.com/fluxml/flux3d.jl
3d-computer-vision 3d-reconstruction 3d-vision-library julia machine-learning point-cloud triangle-mesh
Last synced: about 1 month ago
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3D computer vision library in Julia
- Host: GitHub
- URL: https://github.com/fluxml/flux3d.jl
- Owner: FluxML
- License: mit
- Created: 2020-04-15T05:23:37.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-02-26T00:41:09.000Z (10 months ago)
- Last Synced: 2024-05-08T21:15:50.510Z (8 months ago)
- Topics: 3d-computer-vision, 3d-reconstruction, 3d-vision-library, julia, machine-learning, point-cloud, triangle-mesh
- Language: Julia
- Homepage: https://fluxml.ai/Flux3D.jl/dev/
- Size: 5.58 MB
- Stars: 100
- Watchers: 11
- Forks: 14
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.bib
Awesome Lists containing this project
README
Flux3D.jl
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.
## 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 1.6+ prompt.
```julia
julia> ]
(v1.6) pkg> add Flux3D
```
To install the master branch type the following
```julia
julia> ]
(v1.6) pkg> add Flux3D#master
```
## Examples
## 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)
### TriMesh Transforms (Flux3D.jl and Kaolin)
### Metrics (Flux3D.jl and Kaolin)
## 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)
