Questions regarding to the "Shape Completion" experiments

Hello @jjparkcv and @tschmidt23, thanks for sharing this great work. I’ve finished the model training on “chairs” class and have a few questions about the shape completion experiments in the paper:

1. Are the models in the shape completion experiments trained separately using only partial(single-view) point cloud input? Or I can just reuse the “complete sampling” version of training data(as preprocessing code published in this repo).
2. Do you also use sdf_gt during inference for shape completion(even for noisy depth input)? Is it possible to use zeros as sdf_gt for point cloud input sampled only from the object surface?

For the second question I experimented a little bit, the result is not quite as expected. This is the input point cloud: and this is the reconstructed mesh:

If this is possible, any ideas on what I did wrong?

Thanks a lot!

[JiamingSuen]

After using input data generated from the cpp preprocessing code, the shape completion result is better. It seems that the network is more sensitive to input data than I expected. I’m working on single-view depth input completion experiment and I’m closing this issue for now. However, it would be really nice if you can give me a confirmed answer to my questions.

[tschmidt23]

It looks like there is a scale factor mismatch between your point samples and your reconstruction. For good results, make sure you’re always using SDF samples in the canonical space.