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PyTorch implementation of 3DQD with modifications (Deep Learning Lab - Uni Freiburg)

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model
model
 
 
.gitignore
.gitignore
 
 
Input-Output.png
Input-Output.png
 
 
Most Used Quantizer Embeddings_tsne.png
Most Used Quantizer Embeddings_tsne.png
 
 
Readme.md
Readme.md
 
 
disp_embed.py
disp_embed.py
 
 
display_embeddings.py
display_embeddings.py
 
 
evaluate.py
evaluate.py
 
 
histog.png
histog.png
 
 
interpolate_embeds.py
interpolate_embeds.py
 
 
plot_class_embeds.py
plot_class_embeds.py
 
 
plot_class_embeds_tsne.py
plot_class_embeds_tsne.py
 
 
plot_curves.py
plot_curves.py
 
 
plot_embeds.py
plot_embeds.py
 
 
plot_input_output.py
plot_input_output.py
 
 
poster.pdf
poster.pdf
 
 
poster.png
poster.png
 
 
print_embed_stats.py
print_embed_stats.py
 
 
requirements.txt
requirements.txt
 
 
single_inference.py
single_inference.py
 
 
stacked.png
stacked.png
 
 
test_dataset_paths.txt
test_dataset_paths.txt
 
 
train.py
train.py
 
 
training_curves.png
training_curves.png
 
 
tsdf_dataset.py
tsdf_dataset.py
 
 
utils.py
utils.py
 
 

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Reconstructing 3D Shapes from the ShapeNet Dataset using a Patch Vector Quantized Variational Autoencoder

This Repo is a PyTorch implementation of the PVQVAE from the paper "3DQD: Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process" by Wang et al.

Paper

Major differences and deviations from the paper are discussed in the poster below.

3D Reconstruction poster

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PyTorch implementation of 3DQD with modifications (Deep Learning Lab - Uni Freiburg)

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deep-learning pytorch shapenet 3d-reconstruction variational-autoencoder vector-quantization tsdf

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