ArchVoxel Repo

The model should create voxel representations of buildings using latent diffusion and an intermediate VAE.

The training procedure is as follows:

• Use Vae.py to train the VAE to compress 32³ voxel chunks into a N-dimensional vector. The buildings in /data have a varying size and are split into these chunks. Top row shows reference, bottom row is the reconstruction.
• Latent_preprocess.py takes the whole building and calculates a tensor of VAE-compressed representations. This could be done during the diffusion model training, but it's quite slow and can be preprocessed.
• traindiffusion.py is then used to train the diffusion model based on the latent representation. Intermediate results are saved in the binvox format.
• interpolate.py can be used to visualize the latent space interpolation.

TODOs

• Enlarge train data set.
• The diffusion model has only been trained for 100k steps with a small model. Still figuring out the best model complexity and training scheduling!
• Eventually increase voxel resolution and add more data into the voxel (not just binary visibility) to reconstruct geometry