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Implementation of ICCV2021(Oral) paper - VMNet: Voxel-Mesh Network for Geodesic-aware 3D Semantic Segmentation

License: MIT License

Python 100.00%

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about Matterport3D results

Hi~

Thanks for the great work. I find your work use Matterport3D datatset and get pretty good results showed in the paper.

But I don’t find any code related to Matterport3D in this repository. I wonder if you can share the Matterport3D related code and give a brief description of how to reproduce the results on Matterport3D.

Looking forward to your reply and this will help a lot.

Quadric Error Metrics: contraction of non-connected vertices possible?

Hi there,

Thanks for your amazing work!

I would be glad if you could please answer the following question about the usage of QEM in VMNet.
The publication seems to indicate that for vertex contraction only vertices connected by edges are considered. However, in the original QEM publication, the authors also propose selecting vertex pairs for contraction based on their Euclidean distance. They use a threshold value t for the Euclidean distance.

Using only vertices connected by edges would imply a threshold of t = 0. In prepare_data.py, Tridecimator from VCGlib is called. If I understand the call correctly, the optional argument -e is not passed which specifies the threshold. In the Tridecimator application, the threshold t then defaults to inf, meaning all pairs of vertices would be eligible for contraction.

Therefore I would like to know: Is contraction of non-connected vertices possible in VMNet?

Thanks a lot for your time, Benjamin

Issue of 'input voxels are not valid'

Hi, authors, thanks for sharing your work.

When I tried to train VMNet with my own training data, the valid_idxs is not meet the assert condition, like

VMNet/dataset/scannetv2.py

Line 158 in 6181683

assert sum(valid_idxs) == len(valid_idxs), 'input voxels are not valid'

I think the problem is related to my own data, but the specific reason for such an issue is unclear. Do you have any suggestions about that?

PS: I use the prepare_data.py script to preprocess my own data, as same as the preprocessing procedure for the ScanNet dataset.

The pretrain model has wrong state_dict keys

Hi, I get some issue about

I test the the script with pretrain model

python run.py --test --exp_name test_split --data_path path/to/processed_data

The output logs are:

use_cuda: True
exp_name: test_split
#parameters 17463870
Traceback (most recent call last):
  File "run.py", line 279, in <module>
    test(exp_name, test_files)
  File "run.py", line 139, in test
    model.load_state_dict(checkpoint['model_state_dict'])
  File "/home/keroro/Program_Files/miniconda3/envs/tt/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1052, in load_state_dict
    self.__class__.__name__, "\n\t".join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for VMNet:
	Unexpected key(s) in state_dict: "Geo_branch.mid_geo.geo_0.lin_edge.weight", "Geo_branch.mid_geo.geo_0.lin_edge.bias", "Geo_branch.mid_geo.geo_1.lin_edge.weight", "Geo_branch.mid_geo.geo_1.lin_edge.bias", "Geo_branch.cd_5.geo_0.lin_edge.weight", "Geo_branch.cd_5.geo_0.lin_edge.bias", "Geo_branch.de5_geo.geo_0.lin_edge.weight", "Geo_branch.de5_geo.geo_0.lin_edge.bias", "Geo_branch.de5_geo.geo_1.lin_edge.weight", "Geo_branch.de5_geo.geo_1.lin_edge.bias", "Geo_branch.cd_4.geo_0.lin_edge.weight", "Geo_branch.cd_4.geo_0.lin_edge.bias", "Geo_branch.de4_geo.geo_0.lin_edge.weight", "Geo_branch.de4_geo.geo_0.lin_edge.bias", "Geo_branch.de4_geo.geo_1.lin_edge.weight", "Geo_branch.de4_geo.geo_1.lin_edge.bias", "Geo_branch.cd_3.geo_0.lin_edge.weight", "Geo_branch.cd_3.geo_0.lin_edge.bias", "Geo_branch.de3_geo.geo_0.lin_edge.weight", "Geo_branch.de3_geo.geo_0.lin_edge.bias", "Geo_branch.de3_geo.geo_1.lin_edge.weight", "Geo_branch.de3_geo.geo_1.lin_edge.bias", "Geo_branch.cd_2.geo_0.lin_edge.weight", "Geo_branch.cd_2.geo_0.lin_edge.bias", "Geo_branch.de2_geo.geo_0.lin_edge.weight", "Geo_branch.de2_geo.geo_0.lin_edge.bias", "Geo_branch.de2_geo.geo_1.lin_edge.weight", "Geo_branch.de2_geo.geo_1.lin_edge.bias", "Geo_branch.cd_1.geo_0.lin_edge.weight", "Geo_branch.cd_1.geo_0.lin_edge.bias", "Geo_branch.de1_geo.geo_0.lin_edge.weight", "Geo_branch.de1_geo.geo_0.lin_edge.bias", "Geo_branch.de1_geo.geo_1.lin_edge.weight", "Geo_branch.de1_geo.geo_1.lin_edge.bias", "Geo_branch.cd_0.geo_0.lin_edge.weight", "Geo_branch.cd_0.geo_0.lin_edge.bias", "Geo_branch.de0_geo.geo_0.lin_edge.weight", "Geo_branch.de0_geo.geo_0.lin_edge.bias", "Geo_branch.de0_geo.geo_1.lin_edge.weight", "Geo_branch.de0_geo.geo_1.lin_edge.bias".

What are the Unexpected key(s) in state_dict ? does the VMNet not defined?

When I preprocess the data, I had build VCGlib
vcglib/apps/tridecimator and vcglib/apps/sample/trimesh_clustering , I add environment path by:

export PATH=$PATH:/path/to/vcglib/apps/tridecimator:/path/to/vcglib/apps/sample/trimesh_clustering
# create links
sudo ln -s /path/to/vcglib/apps/tridecimator/tridecimator /usr/local/bin
sudo ln -s /path/to/vcglib/apps/sample/trimesh_clustering/trimesh_clustering /usr/local/bin

But run the preprocess, there is core dumped:

in_path:../scannet/VMtest
out_path:../scannet/VMNet_data/train/
[0.02, 0.04, 30, 30, 30, 30, 30]
Processing ../scannet/VMtest/scene0000_00/scene0000_00_vh_clean_2.ply
curr_dir: ../scannet/VMNet_data/train/scene0000_00
trimesh_clustering: ../../../vcg/simplex/vertex/component.h:75: vcg::vertex::EmptyCore<TT>::ColorType& vcg::vertex::EmptyCore<TT>::C() [with TT = MyUsedTypes; vcg::vertex::EmptyCore<TT>::ColorType = vcg::Color4<unsigned char>]: Assertion `0' failed.
Aborted (core dumped)
trimesh_clustering: ../../../vcg/simplex/vertex/component.h:75: vcg::vertex::EmptyCore<TT>::ColorType& vcg::vertex::EmptyCore<TT>::C() [with TT = MyUsedTypes; vcg::vertex::EmptyCore<TT>::ColorType = vcg::Color4<unsigned char>]: Assertion `0' failed.
Aborted (core dumped)
multiprocessing.pool.RemoteTraceback: 
"""
Traceback (most recent call last):
  File "/home/keroro/Program_Files/miniconda3/envs/tt/lib/python3.7/multiprocessing/pool.py", line 121, in worker
    result = (True, func(*args, **kwds))
  File "/home/keroro/Program_Files/miniconda3/envs/tt/lib/python3.7/multiprocessing/pool.py", line 44, in mapstar
    return list(map(*args))
  File "prepare_data.py", line 226, in process_frame
    old_vertices=vertices[-1])
  File "prepare_data.py", line 173, in quadric_error_metric
    '.ply', '.csv'), old_vertices=old_vertices, new_vertices=vertices_l)
  File "prepare_data.py", line 78, in csv2npy
    with open(in_file_path, 'r') as csvfile:
FileNotFoundError: [Errno 2] No such file or directory: '../scannet/VMNet_data/train/scene0000_00/curr_mesh.csv'
"""

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "prepare_data.py", line 295, in <module>
    pf_pool.map(process_frame_p, file_paths)
  File "/home/keroro/Program_Files/miniconda3/envs/tt/lib/python3.7/multiprocessing/pool.py", line 268, in map
    return self._map_async(func, iterable, mapstar, chunksize).get()
  File "/home/keroro/Program_Files/miniconda3/envs/tt/lib/python3.7/multiprocessing/pool.py", line 657, in get
    raise self._value
FileNotFoundError: [Errno 2] No such file or directory: '../scannet/VMNet_data/train/scene0000_00/curr_mesh.csv'

Where should I set the correct environment path for vcglib/apps/tridecimator and vcglib/apps/sample/trimesh_clustering?

about downloading ScanNet

Hi,
I haven't used ScanNet before, so I'm not familiar with it and need your help. Due to my limited memory space, I want to only download the needed parts of the ScanNet. Since you say:

Our method relies on the .ply as well as the .labels.ply files.

so does it mean that I can download the ScanNet only using this two command?

download-scannet.py -o [directory in which to download] --type _vh_clean_2.ply
download-scannet.py -o [directory in which to download] --type _vh_clean_2.labels.ply

In addition, how much memory space is needed to store the processed data?

What the role of "edge_sampling"?

VMNet/dataset/utils.py

Line 47 in 14e2c25

def edge_sampling(edge_index, cutoff=10, alpha=1.0):

And what is the effect of parameters of "cutoff" and "alpha" on edge_sampling function?

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