search

facebookresearch / pytorch3d

PyTorch3D is FAIR's library of reusable components for deep learning with 3D data
https://pytorch3d.org/
Other
8.83k stars 1.32k forks source link

Code for Experiments: Unsupervised mesh prediction #306

Open vikashranjan opened 4 years ago

vikashranjan commented 4 years ago

Is the code for Experiments with Sphere FC, Sphere GCN, and Voxel GCN mentioned in the paper "Accelerating 3D Deep Learning with PyTorch3D" available?

As I am new to 3d deep learning I would like to go through the code to understand Unsupervised mesh prediction.

gkioxari commented 4 years ago

Our plan is to have this soon! In the mean time we have a tutorial on fitting a mesh from image views using differentiable rendering that could be helpful and that don't deviate much from the learning setting (mesh verts are predicted via a neural network instead of being treated as a variable tensor):

https://github.com/facebookresearch/pytorch3d/blob/master/docs/tutorials/fit_textured_mesh.ipynb

vikashranjan commented 4 years ago

Thanks, looking forward to the code release!

sjcv commented 4 years ago

Hi @gkioxari , just wanted to check on the timeline for this. Thanks again for providing it!

nikhilaravi commented 3 years ago

@sjcv we are planning to open sourcing this as a part of a new projects section in PyTorch3D. We will comment on this task when the code is available.

JobAtom commented 3 years ago

@sjcv we are planning to open sourcing this as a part of a new projects section in PyTorch3D. We will comment on this task when the code is available.

In SphereGCN, 3xGraphConv(512 + 3, 512) + ReLU layer, I am wondering what is 512 + 3 dimension input? Is the 3 means the x, y, z position of input vertex? Thank you.

gkioxari commented 3 years ago

Yes!

sabraha2 commented 3 years ago

Also interested in this! :)

karsgov commented 3 years ago

Any updates here?

moyans commented 2 years ago

Any updates here?