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Thank you for sharing your work, it is actually quite interesting. Would be easy to guide me on how to use your pipeline to train on custom dataset?
My goal is a bit different from what you are doing in your work but at the same time quite similar. What I want to achieve is registering two point clouds so that they merge together rather than aligning. For example look on the two points clouds in the picture below:
The two pcds connect at a specific part but they are two different instances of a bigger pcd.
If the two point clouds are randomly transformed:
I would like to get the transformation matrix which gives me the result in the first image.
I've tested the Teaser++ work and the fpfh feature descriptor vectors in order to extract meaningful correspondences:
but though the teaser+icp can correctly identify the side that the two pcds have the similarities (though this is not the case for all my dataset cases) it tries to align the two point clouds instead of stitching them together:
Thus, the extracted transformation is not correct since it tries to match similar points and not similar points in reverse. I was thinking whether I could use your pipeline for adjusting the correspondence points and how these match to each other, so that to force the alignment that I am seeking.
Thanks.
The text was updated successfully, but these errors were encountered:
Hi @SergioRAgostinho,
Thank you for sharing your work, it is actually quite interesting. Would be easy to guide me on how to use your pipeline to train on custom dataset?
My goal is a bit different from what you are doing in your work but at the same time quite similar. What I want to achieve is registering two point clouds so that they merge together rather than aligning. For example look on the two points clouds in the picture below:
The two pcds connect at a specific part but they are two different instances of a bigger pcd.
If the two point clouds are randomly transformed:
I would like to get the transformation matrix which gives me the result in the first image.
I've tested the Teaser++ work and the fpfh feature descriptor vectors in order to extract meaningful correspondences:
but though the teaser+icp can correctly identify the side that the two pcds have the similarities (though this is not the case for all my dataset cases) it tries to align the two point clouds instead of stitching them together:
Thus, the extracted transformation is not correct since it tries to match similar points and not similar points in reverse. I was thinking whether I could use your pipeline for adjusting the correspondence points and how these match to each other, so that to force the alignment that I am seeking.
Thanks.
The text was updated successfully, but these errors were encountered: