amir-arsalan/synthesize3dviadepthorsil

[CVPR 2017] Generation and reconstruction of 3D shapes via modeling multi-view depth maps or silhouettes

synthesize3dviadepthorsil's Issues

Angles in camPosList

Hello, can you tell me, how to calculate the real angles value from camPosList.txt? And where the reference point is located?
Greetings

Download question

Hello,the download link of pre-trained models is unavailable. so,could you update it again? Thank you

Need input of dimension 4

I have my own trained models (AllVPNet and dropoutNet) trained from ShapeNet Core with my own 20 determined viewpoints with the default settings @epoch80 and batchsize of 4. Both networks are trained on depth maps.

However, I encountered the following error when I tried to output the results for "Random silhouettes/depth maps from user"

Error thrown:
Need input of dimension 4 and input.size[1] == 20 but got input to be of shape: [2 x 1 x 224 x 224]

The input of 20 depth maps are produced with the provided renderDepth program.
The error occurred when trying to use my trained models AllVPNet and also dropoutNet.

However, it worked when I used the originally provided pre-trained model "singleVPNet".
The provided pre-trained model "AllVPNet" and "dropoutNet" also didn't worked.

or could it be that using my own depth maps in "userData" only works for singleVPNet?
If so, how could I get it work with AllVPNet and/or dropoutNet?

I would like to produce results from my own trained network AllVPNet and dropoutNet from an input of 20 depth maps but could not get it to work so far.

Any help is really much appreciated

Thanks in advance

Pre-trained models' link broken

It seems the link to pre-trained models is broken. Would it be possible for you to share it again?

Surface 3D reconstruction of Point cloud

Hello, could you tell me what is the method of converting point cloud into topology when calculating voxel IoU?

How to compute the IoU

Hello, can you give me c code for calculating IoU? Or teach me how to calculate it? I looked at the process of calculating IoU in 3d-r2n2, but I still don't understand it. Looking forward to your reply.

Questions about trainning the network

I have two questions and I really hope to get your help.

First, I have a big problem with training data.
I want to use the ShapeNet Core dataset to repeat your experiments. So I'm going to convert the .mat file into the .ply file.I've found that I can copy the vertex array and faces array directly from the mat file into the ply file. But this approach is too complex. Do you have some simpler ways to do it？

Second, I find the following problems when I training the network of AllVP with a 3D shape(only depths from 20 views). I don't know if it's due to my incorrect input.

/install/torch/install/bin/luajit: /install/torch/install/share/lua/5.1/nn/Container.lua:67:
In 3 module of nn.Sequential:
...torch/install/share/lua/5.1/cudnn/BatchNormalization.lua:44: assertion failed!
stack traceback:
[C]: in function 'assert'
...torch/install/share/lua/5.1/cudnn/BatchNormalization.lua:44: in function 'createIODescriptors'
...torch/install/share/lua/5.1/cudnn/BatchNormalization.lua:60: in function <...torch/install/share/lua/5.1/cudnn/BatchNormalization.lua:59>
[C]: in function 'xpcall'
/install/torch/install/share/lua/5.1/nn/Container.lua:63: in function 'rethrowErrors'
/install/torch/install/share/lua/5.1/nn/Sequential.lua:44: in function 'func'
/install/torch/install/share/lua/5.1/nngraph/gmodule.lua:345: in function 'neteval'
/install/torch/install/share/lua/5.1/nngraph/gmodule.lua:380: in function 'forward'
2_train.lua:300: in function 'opfunc'
/install/torch/install/share/lua/5.1/optim/adam.lua:37: in function 'adam'
2_train.lua:406: in main chunk
[C]: in function 'dofile'
main.lua:130: in main chunk
[C]: in function 'dofile'
...tall/torch/install/lib/luarocks/rocks/trepl/scm-1/bin/th:150: in main chunk
[C]: at 0x00405e90

Thanks for your help.

Pretrained Model Data missing from Drive

In the shared Amazon drive, there is not data
https://www.amazon.com/clouddrive/share/oDnklSMldXWd3CrzSu5ndfl0GMIBffRfvMAFvkWkz5x/folder/5j5Ex9D-RtGRQ-5kYCRYPQ

i just need to just the pre trained model for my project.

Plz make a simple repo for the pre trained model to use. (I'm new to ML/DL)

Question about the loss

Hello,I read the paper "Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks",the loss function which mention inside is to minimize : loss = -DKL(q(Z|X)||p(Z)) + E[logp(x|Z)]
but the loss function in VAE is :
loss = DKL(q(Z|X)||p(Z)) - E[logp(x|Z)]
I am confused about it, I hope you can give me some advice,thank you

getIoU

Hi Amir,

I tried getting the IoU for quantitative evaluations.
Requirements are all met and runs without any error.
Requirements:

1. Visual C++ Redistributable for Visual Studio 2015 (installed-but not from the provided link because a newer version is already installed when installing Visual Studio 2015,
   Version 14.0.24215 was installed instead of the provided Version 14.0.23026)
2. Visual C++ Redistributable Packages for Visual Studio 2013 (installed from the provided link)
3. Python 3.7 (installed)
4. Anaconda with Python 3.6 (installed)

Paths of the .ply files in the test folder are as below:
test/model-userData-1/model-userData-1-or.ply
test/model-userData-1/model-userData-1-rec.ply
test/model-userData-2/model-userData-1-or.ply
test/model-userData-2/model-userData-1-rec.ply
test/model-userData-3/model-userData-1-or.ply
test/model-userData-3/model-userData-1-rec.ply
etc

However, I'm only getting
"----------------average------------------
-nan(ind)"

I believed that the issue might be lying somewhere in the getIoU.exe because it seems to run
without any errors till the end.
However, I could not further verify the problem since the getIoU is a binary.

Have you come across the same problem before?
Any pointers/advise is really much appreciated.

Thanks in advance

Best regards,
Safwan

Training on my own data

When I try and train on my own dataset, I include files for all 20 viewpoints per the README, but when I perform random sampling on the manifold I get results (the 5x5 grids) that are the same for each viewpoint. However when I train with the same arguments on class data from ShapeNet Core through the provided link (e.g. headphones) then I am able to get expected results with different views for each viewpoint. Could you offer any ideas of what I might be doing wrong with my data? Thanks.