Lars Mescheder

@JeremyFisher The watertight meshes are quite big (~500GB) as they contain a lot of vertices and we haven't simplified them to not loose accuracy. If you need the watertight meshes,...

> can you share the data you downloaded? I always failed to download the link provided by the author. > Thank you in advance! If you still have trouble downloading...

Thanks @jnyjxn for your explanation! Check out [the build.sh script](https://github.com/autonomousvision/occupancy_networks/blob/d3a9748a091e5d5acab54691abd06b2f98ae66dc/scripts/dataset_shapenet/build.sh#L45) which contains all steps to build the ShapeNet dataset and which you can adapt to your own dataset. It calls...

Hi @MASTERCHIEF343 , could you please provide more details to reproduce your problem? What did you try to achieve? What is the error message? ...

pykdtree should be automatically built when running ``` python setup.py build_ext --inplace ``` Can you verify that the build step was successful?

Hi @YokkaBear, that depends if your data already consists of watertight meshes. In this case, you can directly use the [sample_mesh.py](https://github.com/autonomousvision/occupancy_networks/blob/master/scripts/dataset_shapenet/build.sh#L45) script. Otherwise, you have to make the meshes watertight...

Hi @YokkaBear , take a look at our processed ShapeNet data you can download. The structure is ``` $CLS/$MODELNAME/pointcloud.npz $CLS/$MODELNAME/points.npz $CLS/$MODELNAME/model.binvox $CLS/$MODELNAME/img/%03d.png ``` where `pointcloud.npz` contains the pointcloud, `points.npz` contains...

Thanks for reporting the issue. I will have a look.

It just reduces the learning rate for those blocks by a factor of 10 (due to the adaptive optimizer RMSProp). We haven't played around with it too much and I...

> I removed the factor 0.1 and changed g_lr and d_lr from 1e-4 to 1e-5, but it cannot converge at all. I don't know the reason. Thanks for reporting your...