pytorch3d
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facebookresearch
Code for Experiments: Unsupervised mesh prediction
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.
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
Hi @gkioxari , just wanted to check on the timeline for this. Thanks again for providing it!
@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.
@sjcv we are planning to open sourcing this as a part of a new
projectssection 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.
