luost26 / score-denoise Goto Github PK

Hi,
I am working on evaluating the point cloud, and 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.

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?
   
   

Sorry to disturb you. Could you pls tell me how to visualize these .xyz documents like your paper?

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.

Hi, I have run your pretrained model to test the PCNet data, but I cannot get the same result as paper. Did I use wrong hyperparameters?

Hi,i am new Python user. When i run “conda install -c pytorch3d pytorch3d==0.5.0” in AnaConda PowerShell Prompt，i will receive this.

my enviorment: windows==11 anaconda==23.7.4 python==3.8.18
what should i do next?

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?

Thanks for your excellent work!

When using test_large.py, the generated point cloud has obvious gaps, which may cause by clustering. I wonder is there a good way to eliminate this, do you have any advice? thx

Hi
Thank you for sharing the code.
However, when I invoke command python test.py --dataset PUNet --resolution 50000_poisson --noise 0.01 --niters 1

I got AttributeError :
`(score-denoise) ola@ola-ASUS-AI:/media/ola/Int2TB/score-denoise$ python test.py --dataset PUNet --resolution 50000_poisson --noise 0.01 --niters 1

Traceback (most recent call last):
File "test.py", line 5, in
import torch.utils.tensorboard
File "/home/ola/anaconda3/envs/score-denoise/lib/python3.8/site-packages/torch/utils/tensorboard/init.py", line 4, in
LooseVersion = distutils.version.LooseVersion
AttributeError: module 'distutils' has no attribute 'version'

(score-denoise) ola@ola-ASUS-AI:/media/ola/Int2TB/score-denoise$
`

I tried to resolve it by
conda install setuptools=59.5.0
and
conda install fairseq
but without success :(
I installed by
conda env create -f env.yml conda activate score-denoise

python test_single.py and python test_large.py work fine !

Any ideas what I can do, please ?

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.

您好，关于点云定性结果的那个渐变颜色是怎么生成的

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.

Why am I getting this error?
cd_loss = validate(it)
File "train.py", line 147, in validate
all_clean = torch.cat(all_clean, dim=0)
RuntimeError: Sizes of tensors must match except in dimension 0. Expected size 37712 but got size 31882 for tensor number 1 in the list.

could u please share the code of making this kind of visual figures?

Is it possible to train a model on larger point clouds, like more than 50k points?

Are you planning to share the code implementation?