Hi Songyou,

Thanks again for sharing the code for your excellent work! I found that the script "eval_meshes.py" is prepared for the learning-based method, but I want to evaluate the mesh result produced by the optimization-based part. Could you please give some suggestions for this?

Looking for your reply. :)

Best, Runsong

In section Installation " Now, you can install PyTorch3D 0.6.0 from the official instruction as follows pip install pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py38_cu102_pyt190/download.html " but I can only get pytorch3d 0.0.1 to 0.3.0 from the above address. I want to check whether we should install pytorch3d 0.3.0 with the above command or manually install pytorch 0.6.0 from official instruction?

P.S. I tried pytorch3d 0.3.0 but it ends up with ImportError: libcudart.so.10.1: cannot open shared object file: No such file or directory

Thanks!

Hi Songyou,

Thanks again for sharing the code for your excellent work! It helps a lot!

I am trying to implement optimization-based multi-view images reconstruction. I used your PixelNeRFDTUDataset to load DTU. However, I found there is a function called uv_creation is missing. And I am kind of stuck here. Could you please kindly provide your code for that function?

Thanks a lot!

Thanks for sharing this great work! It seems that the interpolation method used in the point_rasterize and grid_interp function is based on the absolute distance of the point to the adjacent grid point. Have you ever tried other interpolation method such as trilinear interpolation? Do different interpolation methods affect the results?

Hi Songyou,

Thanks again for sharing the code for your excellent work! Could you please provide the pretrained model of optimization-based version that are reported in your paper (there are three datasets: dfaust, thingi, deep_geometric_prior_data).

Looking for your reply. :)

Best, Runsong

Thanks for sharing this great work! I am a little confused about the rasterize function. It seems that the weight is the product of the absolute distance, and the value on the gird is the weighted sum of all the relevant points. Does the grid value need to be normalized? Because I have found that when I rasterize the parameters of the points to the grid, the value on the grid increases as the number of the sampled points increases. But when I normalize the value on the grid, there is an error of marching cube. Do you have any advice or is there any relationship between the number of sampling points and the grid size?

Hi Songyou,

Thanks for sharing your code for the excellent work! I am curious about the optimization-based part and I have two questions. Can you help me? thanks a lot in advance. :)

• Do you use the whole point cloud? For example, the "daratech" shape(SRB dataset) contains 71265 point cloud, you put all the point clouds for your methods or you downsample them first.
• Can you provide the parameter for Poisson Surface Reconstruction(e.g. depth) and parameter for minimum spanning tree method if possible.

Looking forward to your reply!

Best, Runsong

Hello, I want to download the demo data，but falied to establish SSL connection. Could you please upload a copy to GoogleDrive at the same time?
Thanks！

wget https://s3.eu-central-1.amazonaws.com/avg-projects/shape_as_points/data/demo.zip

--2021-11-10 10:33:21--  https://s3.eu-central-1.amazonaws.com/avg-projects/shape_as_points/data/demo.zip
Proxy tunneling failed: Bad GatewayUnable to establish SSL connection.

Thank you for great work

I'm curious about what train_overfit.lst and test_overfit.lst mean in the preprocessed dataset which can be downloaded in Dataset for Learning-based Reconstruction?

I cannot find any clue in scripts/preprocess_shapenet.py.

Thank you

I use SAP to reconstruct mesh from depth map input, while the result is somekind weird. It seems that SAP try to reconstruct closed shape. For those only one side oriented point clouds extracted from depth map SAP additionally generates some discrete faces. Is there any way to remove these faces?

Hey, Thank you for publishing the code and congratulations on getting the NiPS oral! Could you please provide some comparison of the Optimization-based vs. Learning-based results or at least provide some insight into what works better under what circumstances. (I looked through the paper and SM and couldn't find this, but if it exists and I've missed it, could you please point me in the right direction?)

Thanks, Eliahu

got error when trying to make parameter o3d_show as True in yaml file.

aga/res_32 --train:lr_pcl 0.002000 --data:object_id -1 Changing model:grid_res ---- 256 to 32 Changing model:psr_sigma ---- 2 to 2 Changing train:input_mesh ---- to None Changing train:total_epochs ---- 4000 to 1000 Changing train:out_dir ---- /dsk1/shape_as_points/after/aga to /dsk1/shape_as_points/after/aga/res_32 Changing train:lr_pcl ---- 2e-2 to 0.002000 Changing data:object_id ---- 0 to -1 /dsk1/shape_as_points/after/aga/res_32 Segmentation fault (core dumped)

Couldn't find the right solution to the questions, how can this be fixed?

Good afternoon, I have a few questions, I am trying to do a point cloud processing cycle for my master's work at the university and I will be very glad if you answer my questions:

1. From your article, I did not quite understand what exactly is being trained in the model, could you clarify which parameters are being improved in the learning process?
2. Do I understand correctly that you trained your model on all three datasets? I am trying to optimize a point cloud in the form of an airplane, and the result of your model does not turn out to be as high-quality as I would like. I want to train the model on 3D figures of planes, BUT there are already planes in the ShapeNet dataset on which you trained your model, and despite this, the result is still not very good (the result is attached to the cart). Does it make sense to retrain your model ONLY on airplanes to improve the result ? The original number of points in the aircraft model is about 60000+-.