Questions about partition on a large colored point cloud about superpoint_graph

[loicland]

Right, an error happens if the size of the file is a multiple of the batch size. Good catch! It is fixed in the latest commit.

Also please use the newest commit and use partition.py instead of partition_X.py as stated in the updated README

I see that there is a problem displaying the correct pruning %, it is now also fixed

Now to your point clouds. This has nothing to do with color as far as I can tell . Your first file (691892 points) is already subsampled with at least a 5cm grid (actually about 12 cm), so the pruning does nothing.

The second one (635137) is very small, about 50 cm of length with a huge precision. Hence the pruning decimates the cloud completely.

[sycmio]

Thanks for your answer! Now I can run your partition code successfully. But there are still some little problems: 1. The pruning % is still not correct 2. I still got a UserWarning: genfromtxt: Empty input file: although the code can keep running. 3. Seems that the partition on our point cloud ended because the max iteration was reached. Does it mean our point cloud is hard to compute the SPG?

[loicland]

1. you need to recompile libply_c.so

2. Yes, that's expected. I'll look at how to shut down this warning later.

3. I wouldn't worry about it, 5 iterations of cut pursuit is more than enough in most case. Is the partition satisfactory? Else post the cut pursuit steps please.

It is usually beneficiary both in processing speed and precision to subsample the input point cloud with --voxel_width in partition.py. You can upsample the results on the original point cloud with the parameter upsample 1 in /partition/visualizer.py

[zeroAska]

Hi loicland,

I have a similar issue there when running partition.py on semantic3d test_full:

=================
test_full/

1 / 16---> marketplacefeldkirch_station4_intensity_rgb
reading the existing feature file...
reading the existing superpoint graph file...
Timer : 0.0 / 0.0 / 0.0
2 / 16---> stgallencathedral_station6_intensity_rgb
reading the existing feature file...
reading the existing superpoint graph file...
Timer : 0.0 / 0.0 / 0.0
3 / 16---> sg27_station6_intensity_rgb
reading the existing feature file...
reading the existing superpoint graph file...
Timer : 0.0 / 0.0 / 0.0
4 / 16---> marketplacefeldkirch_station7_intensity_rgb
reading the existing feature file...
reading the existing superpoint graph file...
Timer : 0.0 / 0.0 / 0.0
5 / 16---> sg28_station5_xyz_intensity_rgb
reading the existing feature file...
reading the existing superpoint graph file...
Timer : 0.0 / 0.0 / 0.0
6 / 16---> stgallencathedral_station3_intensity_rgb
reading the existing feature file...
reading the existing superpoint graph file...
Timer : 0.0 / 0.0 / 0.0
7 / 16---> test_full
creating the feature file...
Traceback (most recent call last):
File "partition/partition.py", line 132, in
xyz, rgb = read_semantic3d_format(data_file, 0, '', args.voxel_width, args.ver_batch)
File "/home/v9999/perl_code/rvsm/superpoint_graph/partition/provider.py", line 228, in read_semantic3d_format
xyz_full = np.array(vertices[:, 0:3], dtype='float32')
IndexError: too many indices for array

[loicland]

Hi,

Can you print the size of vertices just before the bug?

Also priniting the batch number :

print("%d" % (i_batch))

With i_batch a counter incremented at the beginning of the while True: loop

[zeroAska]

Hi loicland,

Here are the prints before the error in provider.py:

7 / 16---> test_full
    creating the feature file...
[provider.py] length of vertices is 15
[provider.py] i_rows is 0, ver_batch is 5000000
Traceback (most recent call last):
  File "partition/partition.py", line 132, in <module>
    xyz, rgb = read_semantic3d_format(data_file, 0, '', args.voxel_width, args.ver_batch)
  File "/home/v9999/perl_code/rvsm/superpoint_graph/partition/provider.py", line 231, in read_semantic3d_format
    xyz_full = np.array(vertices[:, 0:3], dtype='float32')
IndexError: too many indices for array

[loicland]

Hi,

can you add the following line 238 of /partition/provider.py, just before xyz_full = ...

print(vertices.shape)

and report the log after:

7 / 16---> test_full
    creating the feature file...

I am trying to reproduce your bug but I need these information.

Did you use the default values for ver_batch?