sh8 / repose Goto Github PK

Hello, thanks for sharing such great work!

So I am able to use neural-renderer example code to generate rendering results with torch==1.2 installed. While the REPOSE's prerequisite is Pytorch == 1.9.0, and if I install neural-renderer under torch==1.2 and upgrade to torch==1.9 then I will encounter error:
import neural_renderer.cuda.load_textures as load_textures_cuda ImportError: /usr/local/lib/python3.7/dist-packages/neural_renderer/cuda/load_textures.cpython-37m-x86_64-linux-gnu.so: undefined symbol: _ZN6caffe26detail37_typeMetaDataInstance_preallocated_32E
when running neural-renderer example code. How should I solve this conflict?

Hi, thanks your work, this is a meaningful and interesting.
but I want to know that the code update work has been completed? How can I start training?

Hi, this is a great work.
As you metioned in the paper, you did experiments on Occ-Linemod, using the pvnet results as the initial pose. Can you please share the pvnet initial result? Any format is okay, Thanks.

I'm sorry to bother you, but the download link for all the datasets has expired. Could you please provide the available link for downloading the datasets?

Hi,

Thank you for your excellent work!
I am a little confused about the PVNet's initial pose for LINEMOD and LINEMOD Occlusion. How could I find them?

Also, for the initial pose result in YCB-V based on PoseCNN you used, how could I find it?

Best,
Rui

Hi! Thanks for your great works

I have one question.

I got this error from "pip install -r requirements.txt"
ERROR: Could not find a version that satisfies the requirement soft-renderer==1.0.0 (from versions: none)
ERROR: No matching distribution found for soft-renderer==1.0.0

and I cannot find any package name "soft-renderer" in pypi either.

What should I do?

I like how this approach does not require a texture of the object's 3D model. As you mention in the paper, this is a typical situation, especially for objects that are challenging to scan.

In some cases, there are also no real images of the object to train on. In these cases, synthetic data with domain randomization is often used to render images with random textures applied to the object's 3D model.

If I understand correctly, RePOSE trains on real images of the object. Have you done any experimentation with just domain-randomized synthetic data? Any intuition on whether this will work?

Hi, thank you for your awesome work! But when I try to run the command "pip install -r requirements.txt", and it just shows that

"ERROR: [email protected]:sh8/rdopt.git@7601bca4818a03ef1ace1e0c1df396ccec56003f#egg=camera_jacobian is not a valid editable requirement. It should either be a path to a local project or a VCS URL (beginning with bzr+http, bzr+https, bzr+ssh, bzr+sftp, bzr+ftp, bzr+lp, bzr+file, git+http, git+https, git+ssh, git+git, git+file, hg+file, hg+http, hg+https, hg+ssh, hg+static-http, svn+ssh, svn+http, svn+https, svn+svn, svn+file)."

How could I fix this? Thank you very much!

I have ran the code, and test the ADD(-S) in every object in linemod dataset(not occ), the cache file(pvnet result) of object driller seems to be wrong. The test metric ADD(-S) of driller is just 41.76%.
Could you please upload the cache file of object driller again?

Hi, very nice work.
And Here when I run your visulization code, it shows:

It seems that it has no key of 'R_all'.

And could you please share about your training codes? Thanks!