Hi,

thanks for making this great work available here!

I would like to test one of the pretrained models on my own dataset.

However, I am a bit lost in writing a corresponding config file.

For testing, I simply copied a single points.npz file from ShapeNet to a new folder.

I wrote the following myConfig.yaml file:

data: classes: [''] path: /home/raphael/data pointcloud_n: 10000 pointcloud_file: points.npz voxels_file: null points_file: null points_iou_file: null training: out_dir: out/mine test: model_file: https://s3.eu-central-1.amazonaws.com/avg-projects/convolutional_occupancy_networks/models/pointcloud/shapenet_3plane.pt generation: generation_dir: generation

When running python generate.py config/myConfig.yaml I get the following error:

cfg_special = yaml.load(f) /home/raphael/remote_python/convolutional_occupancy_networks/src/config.py:33: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details. cfg = yaml.load(f) Traceback (most recent call last): File "generate.py", line 38, in dataset = config.get_dataset('test', cfg, return_idx=True) File "/home/raphael/remote_python/convolutional_occupancy_networks/src/config.py", line 134, in get_dataset inputs_field = get_inputs_field(mode, cfg) File "/home/raphael/remote_python/convolutional_occupancy_networks/src/config.py", line 202, in get_inputs_field 'Invalid input type (%s)' % input_type) ValueError: Invalid input type (img)

Could you give me a hint on how to achieve what I want to do?

Kind regards!

Hi: thanks for making this great work available here! I want to know the total training time for 3000 epochs for the model in this paper (or if you use a single GPU ? ). I find it's so long for me to train the model on a single 2080Ti.

Hi there, thank you for making the code public!

I have a question about data sampling strategy in synthetic indoor scene dataset.

After placing the object in the room, do you randomly sample points and query their ground truth occupancy in the whole scene? If so, wouldn't the occupancy data severely biased toward empty (since most space inside the room should be empty)?

Another question is that: how many point-occupancy pairs do you sample for one batch?

I'm not able to clearly locate these details in the code, so I raise the issue to ask. Thank you for your time!

Thank you very much for this repo and your paper.

This is not so much an issue as much as it is shared tips for others who, like me, may be struggling to run this code on latest Nvidia graphics cards. My specs are: Ubuntu 18.04 2 x Nvidia RTX 3090 g++ 9.4/gcc 8.4/ninja 1.8

Specifically I had two main issues:

1. running the code according to the instructions (installing from environment.yaml) led to unspecified cuDNN error that could be traced to incompatibility between my RTX3090 and the required PyTorch 1.5.
2. installing all requirements manually including the latest PyTorch led to errors when running setup.py.

My solution was:

1. (this may not be required but I include it as it is how my system is set up) install cuda 11.1 and ensure the $PATH and $LD_LIBRARY_PATH environment variables are configured correctly (see instruction 9.1.1 here - but pointing to cuda 11.1 instead of 11.4)
2. manually create and activate a brand new conda environment: conda create -n my_conv_onet python=3.6 and conda activate my_conv_onet
3. use pip to install numpy and cython: pip install numpy cython
4. modify the setup.py file by: a. remove the import for torch.utils.cpp_extension b. at the end of the file, remove the cmd_class argument from the setup function
5. now run python setup.py build_ext --inplace
6. install PyTorch as per Torch website instructions for installing PyTorch with cuda 11.1: pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
7. install torch-scatter as per Torch Scatter website instructions for installing with cuda 11.1: pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html
8. install remaining dependencies with pip: pip install matplotlib tensorboardx trimesh pyyaml tqdm

This should then provide you with a fully installed environment from which to run the code. I will print my conda list in a comment below.

gcc: error: /home/deploy/tmp/pycharm_project_794/convolutional_occupancy_networks/build/temp.linux-x86_64-3.7/src/utils/libkdtree/pykdtree/kdtree.o: No such file or directory gcc: error: /home/deploy/tmp/pycharm_project_794/convolutional_occupancy_networks/build/temp.linux-x86_64-3.7/src/utils/libkdtree/pykdtree/_kdtree_core.o: No such file or directory

In the line mentioned below, which is a part of documentation, it is mentioned that you are using conditional batch norm. However, in the code, you are using residual blocks without any batch norm. It seems that it is a legacy documentation. Please let me know, if you are actually intending to use batchnorm (conditional or otherwise).

Edit: I would really appreciate if you can also give some intuition about why did you not use batch normalization at all?

https://github.com/autonomousvision/convolutional_occupancy_networks/blob/1ef59d0d7e099a7723a3c317cdc97403bbf91f63/src/conv_onet/models/decoder.py#L9

Hi, I'm wondering why normalize_3d_coordiante is needed if the point coordinate is in range of [-0.5, 0.5]? https://github.com/autonomousvision/convolutional_occupancy_networks/blob/f44d413f8d455657a44c24d06163934c69141a09/src/encoder/voxels.py#L74

Hi, thanks for sharing your great work!

I have a few questions:

1. You mentioned that "our method is independent of the input representation" in the paper. I am curious to know that if it is possible to use multi-view images as input in your framework. If the answer is yes, could you provide a hint how to implement that?

2. If train your method in a real-world dataset and test on another real-world dataset, do you think it will still work?

3. As mentioned in Sec. 6 of the supplementary material, you "randomly sample one point within the scene as the center of the crop". May I know how you balance the positive and negative samples (i.e., gt occupancy value 1 and 0) with random sampling?

Looking forward to your reply. Thanks.

Hi, I just tried training the synthetic indoor scene with datasets downloaded from the link. It worked well. I want to compare the generated mesh with the ground-truth mesh, however, what I can find is some point clouds that seem to be the ground truth. Therefore, could you please help me with the ground-truth mesh of the synthetic indoor scene? Thanks a lot.

https://github.com/autonomousvision/convolutional_occupancy_networks/blob/f15b97da2c5995598537c8f832e52e95c0b09236/scripts/dataset_synthetic_room/build_dataset.py#L352

I think it should be if walls[3] == 1. Is it a typo?

Hi, Thank you for making the code for this nice project publicly available. If I have a point cloud stored in a generic .ply file, how can I process it in such a way that I can run inference with the model that was trained on matterport? So is there some "build_dataset.py" file that is not for a specific dataset, but can just be used for a normal .ply file? Best, Duncan

Hi Peng, Thanks for your great work. I ran the "python generate.py configs/pointcloud_crop/demo_matterport.yaml" and got the question of "RuntimeError: cannot join current thread", detailed as follows:

Could you help me in that regard? Thank you.

Hi,

I have successfully trained ConvONet on my own object datasets before with nice results! I have used the sample_mesh.py script from ONet to generate pointcloud.npz and points.npz (occupancy points) files.

I would now like to train the network to reconstruct very large scenes, for which I have ground truth meshes for some of them. How do I go about this?

Can I simply provide one points.npz and pointcloud.npz file per scene? How do I make sure to have enough occupancy samples per crop? Should I simply make sure to have 100k occupancy points per crop defined by voxel_size * resolution? Or do I need to do the cropping myself?

Kind regards