Hi, thanks for sharing your work. Is it easy to give some guidance or do you have any tutorial to test/evaluate the pre-trained models on two individual given point clouds?

Hi ,thanks for your amazing work.

However , I have met a problem of kitti dataset result reproduce. I followed your code to train and test on kitti dataset , but my result seems always lower than the result in your paper. I can achieve Registration recall of 99.8 and RTE 6.8 same as yours ,but my result of RRE is about 0.33 which is much higher than 0.24 in your paper.

Can you give me some idea of how to reproduce the same result ?

Hi , thanks for your great work. And I would like to know how can I get this kind of visualization result in your paper. When I use open3d to draw the point clouds , it seems very ugly, so I'm wondering how can I get your visualization result. It would be very grateful.

Hello, author: I ran the python files in the Kitti dataset and ModelNet dataset folders according to your requirements, but they did not work and were not classified into the ones you described. Is there a specific method? thank you!

Hi there,

Really appreciate releasing your amazing work! I'm quite new to point clouds and I have some questions regarding downsampling of the point clouds.

Take 3DMatch as an example. The original raw dataset downloaded from PREDATOR is quite dense, and is preprocessed so that each grid size is 2.5cm. In your work, you further subsample the point clouds using KPConv (the first level downsampled points). My first question is what's the purpose of subsampling? Would a denser point cloud be more informative for registration?

Also, is there a specific reason you downsample the points further for dense point correspondences as I believe the preprocessed input data is already of density 2.5cm?

Last, is there a quick way to visualize the registration result for the 3DMatch benchmark? By running eval.py and test.py it seems to only produce the quantitative metrics.

Thanks for your patience and reply in advance!

Hi,

I have a question regarding the D.2. section from the Supplementary material.

Could you please share which 3DMatch files you use for the evaluation?

• Do you use those with overlap > 30%?
• Do you use consecutive pairs of point cloud indices (the original 3DMatch filters the pairs (i,j) with i+1 < j)?

You mention that you use the files from PREDATOR, but PREDATOR has more pairs than the original 3DMatch dataset. Can you please elaborate?

Thank you in advance for you response. Hopefully we can clear all this confusion of the registration pairs used in the datasets. Every paper seems to do it a bit differently.

in line 74 in experiments/geotransformer.3dmatch.stage4.gse.k3.max.oacl.stage2.sinkhorn/model.py , transform = data_dict['transform'].detach(), and in line 110 you use gt transform in get_node_correspondences. however If i don't know how the transform matrix of src to ref, how can I use demo.py

Hello, thank you for you amazing work again. When I evaluated on 3DMatch, I found the RR reported by test.py and eval.py are very different, what makes this difference?

First of all, thanks for your excellent work! Radius search in KPConv is quiet slow for large scale point clouds and it usually takes seconds in my own experiments. Is there a more efficient way for radius search, such as a version that can run on GPU. Hope to get your answer！

Dear authors,

Thanks for open-sourcing your amazing work. I'm experimenting with RANSAC and I noticed some unexpected results compared to the paper.

What I've done is first change the number of iterations in RANSAC to 50k. The evaluation runs extremely slowly (approx. 16 hrs in contrast to about 1 hr for local to global), and I only got performance similar to local to global. I also tried to change the number of points in RANSAC to a larger number (by default it is set to 3), such as 250, 500, 1000, etc. I did not achieve a significant gain in performance, even the recall collapses to zero if the number of points is set too big (like 5k). This is contradicting to what's reported in the paper, as the performance is reported to keep improving as the number of samples increases.

I would be super grateful if you could guide me through what I've done wrong here. Thanks in advance for your patience and help!

Hello, sir. The question is about creating the code runtime environment. After reinstalling CUDA and downloading torch, I executed 'python setup.py build develop' and it still report the error, 'RuntimeError: Error compiling objects for extension'.

Traceback (most recent call last): File "setup.py", line 5, in setup( File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools_init_.py", line 87, in setup return distutils.core.setup(**attrs) File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools_distutils\core.py", line 148, in setup return run_commands(dist) File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools_distutils\core.py", line 163, in run_commands dist.run_commands() File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools_distutils\dist.py", line 967, in run_commands self.run_command(cmd) File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools\dist.py", line 1214, in run_command super().run_command(command) File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools_distutils\dist.py", line 986, in run_command cmd_obj.run() File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools_distutils\command\build.py", line 135, in run self.run_command(cmd_name) File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools_distutils\cmd.py", line 313, in run_command self.distribution.run_command(command) File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools\dist.py", line 1214, in run_command super().run_command(command) File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools_distutils\dist.py", line 986, in run_command cmd_obj.run() File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools\command\build_ext.py", line 79, in run _build_ext.run(self) File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools_distutils\command\build_ext.py", line 339, in run self.build_extensions() File "F:\Anaconda3\envs\geotransformer\lib\site-packages\torch\utils\cpp_extension.py", line 670, in build_extensions build_ext.build_extensions(self) File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools_distutils\command\build_ext.py", line 448, in build_extensions self._build_extensions_serial() File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools_distutils\command\build_ext.py", line 473, in _build_extensions_serial self.build_extension(ext) File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools\command\build_ext.py", line 202, in build_extension _build_ext.build_extension(self, ext) File "F:\Anaconda3\envs\geotransformer\lib\site-packages\setuptools_distutils\command\build_ext.py", line 528, in build_extension objects = self.compiler.compile(sources, File "F:\Anaconda3\envs\geotransformer\lib\site-packages\torch\utils\cpp_extension.py", line 643, in win_wrap_ninja_compile _write_ninja_file_and_compile_objects( File "F:\Anaconda3\envs\geotransformer\lib\site-packages\torch\utils\cpp_extension.py", line 1250, in _write_ninja_file_and_compile_objects _run_ninja_build( File "F:\Anaconda3\envs\geotransformer\lib\site-packages\torch\utils\cpp_extension.py", line 1555, in _run_ninja_build raise RuntimeError(message) from e RuntimeError: Error compiling objects for extension

Is this because I use win10? Or other issues caused the error?

Thank you for your excellent work. When I use the LGR training model without changing the code, the result on 3DMatch (about 91.0%) was still quite different from the model you provided (92.5%)