D:\anaconda\anaconda\envs\score\python.exe D:/code/score-denoise-main/score-denoise-main/test.py [2022-07-16 20:04:07,207::test::INFO] [ARGS::ckpt] './pretrained/ckpt.pt' [2022-07-16 20:04:07,207::test::INFO] [ARGS::input_root] './data/examples' [2022-07-16 20:04:07,208::test::INFO] [ARGS::output_root] './data/results' [2022-07-16 20:04:07,208::test::INFO] [ARGS::dataset_root] './data' [2022-07-16 20:04:07,208::test::INFO] [ARGS::dataset] 'PCNet' [2022-07-16 20:04:07,208::test::INFO] [ARGS::tag] '' [2022-07-16 20:04:07,208::test::INFO] [ARGS::resolution] '10000_poisson' [2022-07-16 20:04:07,208::test::INFO] [ARGS::noise] '0.01' [2022-07-16 20:04:07,208::test::INFO] [ARGS::device] 'cuda' [2022-07-16 20:04:07,208::test::INFO] [ARGS::seed] 2020 [2022-07-16 20:04:07,208::test::INFO] [ARGS::ld_step_size] None [2022-07-16 20:04:07,208::test::INFO] [ARGS::ld_step_decay] 0.95 [2022-07-16 20:04:07,209::test::INFO] [ARGS::ld_num_steps] 30 [2022-07-16 20:04:07,209::test::INFO] [ARGS::seed_k] 3 [2022-07-16 20:04:07,209::test::INFO] [ARGS::niters] 1 [2022-07-16 20:04:07,209::test::INFO] [ARGS::denoise_knn] 4 [2022-07-16 20:04:10,298::test::INFO] ld_step_size = 0.20000000 [2022-07-16 20:04:10,345::test::INFO] boxunion [2022-07-16 20:04:18,952::test::INFO] box_push [2022-07-16 20:04:24,497::test::INFO] column_head [2022-07-16 20:04:30,082::test::INFO] cylinder [2022-07-16 20:04:35,609::test::INFO] dragon [2022-07-16 20:04:41,134::test::INFO] galera [2022-07-16 20:04:46,660::test::INFO] happy [2022-07-16 20:04:52,189::test::INFO] icosahedron [2022-07-16 20:04:57,717::test::INFO] netsuke [2022-07-16 20:05:03,248::test::INFO] star_smooth Loading: 100%|██████████| 10/10 [00:00<00:00, 33.74it/s] Loading: 100%|██████████| 10/10 [00:00<00:00, 34.59it/s] Loading: 100%|██████████| 10/10 [00:06<00:00, 1.48it/s] Evaluate: 100%|██████████| 10/10 [00:11<00:00, 1.18s/it] [2022-07-16 20:05:27,919::test::INFO] cd_sph p2f boxunion 0.000311 0.000019 box_push 0.000357 0.000229 column_head 0.000530 0.000344 cylinder 0.000321 0.000067 dragon 0.000317 0.000208 galera 0.000383 0.000300 happy 0.000302 0.000217 icosahedron 0.000331 0.000065 netsuke 0.000242 0.000148 star_smooth 0.000276 0.000110 [2022-07-16 20:05:27,919::test::INFO] Mean cd_sph 0.000336946515 p2f 0.000170847624

Process finished with exit code 0

I used the hyperparameter you saved, but the P2M accuracy is abnormal. P2M in the output are quite different from those given in your paper.May I ask why this is?

Hi @luost26,

Thank you for sharing your implementation! I just have a question about the supervised (and self-supervised) loss function. In https://github.com/luost26/score-denoise/blob/main/models/denoise.py line 77, what is the purpose of self.dsm_sigma? I was not able to find this in the paper.

Furthermore, in equation 3 of the main paper, you take the expectation with respect to the distribution N(x_i). But in the code this is a straightforward average so is this a uniform distribution?

Thank you! D.

In the paper, 20 meshes of PU dataset were used to train, but in the code, there are 40 meshes. Can it influence the result? I have retrain the network and get worse results.

Hello:) I launched your model on a large number of points (200 thousand +-) on an airplane model, but there were no improvements to the model. On the left, the point cloud before the change, on the right after the change. I also tried to reduce the number of points to 70 thousand and there was no result either.

1. Why does the model not give results on a large number of points?
2. Does it make sense to try to train the model on your loyal data?

Thank you for your excellent work!

1. I want to create my own dataset. I wonder if I just need to provide a clean point cloud? I found that this model seems to include a function that adds noise to the point cloud.

2. For some baselines in the article, such as MRPCA and GLR, I found that there seems to be no publicly available code for these methods. Did you implement them yourself?

Hi, great work!

I am very curious about the unsupervised adaptation. Unfortunately, the supplementary material you mentioned is not available in the paper(v3) in arxiv.

It would very nice if you could share the supplementary part. Looking forward to the implementation as well.

 python test_large.py 

Running KMeans to construct clusters...
Denoise Clusters:   0%|                                                                                                                                                                                                                                    | 0/69 [00:00<?, ?it/s]
Traceback (most recent call last):


RuntimeError: Given groups=1, weight of size [128, 63, 1], expected input[100000, 66, 4] to have 63 channels, but got 66 channels instead

Hi, I am working on evaluating the point cloud, and I think maybe it is possible to use the “score“ idea to give each point a “convincing score”. So I have read your code, but actually it is a little hard to understand the whole part. I want to ask whether it is possible to make score separately be output? If I understand it correctly, the first step is to use the feature net and use the feature net output with the original point could to get the score. When is the score calculated out? The final score will be like the equation 4 in the thesis, and is it actually associated to each point? Thanks in advance.