My training vs Paper training #382
Comments
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I would guess, the belief looks really good, but not your pnp results, can you visualize the raw points? I think your cuboid order is off @nv-jeff I think this is similar to the error the other person is having. |
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I am not familiar with the raw points, can you elaborate on how to get them?
Based on this generated data the
# Cuboid dimension in cm x,y,z
dimensions: {
# For my training
"cracker": [7.179999828338623, 16.403600692749023, 21.343700408935547],
# For paper weights
#"cracker": [16.403600692749023, 21.343700408935547,7.179999828338623]
}I had to follow the orientation that my 3D object had in meshlab as you can see in my first meshlab screenshot available in this issue (top left corner [size is in meters]) . You guys probably had a different one in your 3D model.
I think that the output now is closer to your paper training weight, but yours is something else that I am not capable to achieve :(
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can you draw these https://github.com/NVlabs/Deep_Object_Pose/blob/master/ros1/dope/src/dope/inference/cuboid_pnp_solver.py#L101 obj_2d_points so I think when you call obj_2d_points and obj_3d_points; the points are not aligned, for example let say the top right front is at index 0 in obj_2d_points but index 1 in obj_3d_points, that will cause your issues. possibly might be caused by not using the right of these 2. Which now I do not remember which is which. https://github.com/NVlabs/Deep_Object_Pose/blob/master/ros1/dope/src/dope/inference/cuboid.py#L82 @nv-jeff Maybe we should clear all of this so there is only one way to generate a set of cuboid points. |
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Hello again, and sorry for the delayed response.
I think this is it for now, I will continue to play with DOPE |
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ahhh this looks amazing good job, sorry I will check with @nv-jeff to see if he could make the update. |
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Good sleuthing! I will take a look at the code and refactor the changes mentioned above. |
and others
After playing around with DOPE I decided to train the cracker 3D object to compare my training with the weights obtained from the training for the project paper.
3D ObjectDatasetI generated 83K images with
blenderprocfollowing the guidelines provided in this repo/issues.TrainingI have been training this object for a couple days and my loss looks like this:
The loss seems to be stuck around the
0.007value after approximately200 epochs. Is it still decreasing?I am concerned because the results are far from the obtained in the paper.
Some inferences with my training:
Some beliefs with my training:
00000_belief:
00100_belief:
00200_belief:
00300_belief:
What can I do to improve this result, any suggestion?
Thanks in advance 💪
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