Gorilla-Lab-SCUT / BiCo-Net

Code for "BiCo-Net: Regress Globally, Match Locally for Robust 6D Pose Estimation"
MIT License
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How to get right visualization results in YCB and LineMOD dataset? #6

Open ZJU-PLP opened 1 year ago

ZJU-PLP commented 1 year ago

@aragakiyui611 Hi, dear author:  When I want to reproduce the visualization results of BiCo-Net, I meet one problem. Could you mind helping me to solve this issue?(I also have sent an email to your mailbox).

 I cannot get the right visualization results. Specially, the prediction point cloud cannot be visualized right while the target point cloud can do.

target_t = target_t + centroid
out_t = out_t + centroid
target = torch.bmm(model_points, target_r.transpose(2, 1)) + target_t # (1, 2620, 3)
pred = torch.bmm(model_points, out_R.transpose(2, 1)) + out_t # (1, 2620, 3)

Visualization results: example 1: image example 2: image

ethanshenze commented 1 year ago

@aragakiyui611 Hi, dear author:  When I want to reproduce the visualization results of BiCo-Net, I meet one problem. Could you mind helping me to solve this issue?(I also have sent an email to your mailbox).

 I cannot get the right visualization results. Specially, the prediction point cloud cannot be visualized right while the target point cloud can do.

target_t = target_t + centroid
out_t = out_t + centroid
target = torch.bmm(model_points, target_r.transpose(2, 1)) + target_t # (1, 2620, 3)
pred = torch.bmm(model_points, out_R.transpose(2, 1)) + out_t # (1, 2620, 3)

Visualization results: example 1: image example 2: image

Hello, I also encountered the problem of not being able to visualize, could you please share your code? Hope to study together.

ZJU-PLP commented 1 year ago

@aragakiyui611 Hi, dear author:  When I want to reproduce the visualization results of BiCo-Net, I meet one problem. Could you mind helping me to solve this issue?(I also have sent an email to your mailbox).  I cannot get the right visualization results. Specially, the prediction point cloud cannot be visualized right while the target point cloud can do.

target_t = target_t + centroid
out_t = out_t + centroid
target = torch.bmm(model_points, target_r.transpose(2, 1)) + target_t # (1, 2620, 3)
pred = torch.bmm(model_points, out_R.transpose(2, 1)) + out_t # (1, 2620, 3)

Visualization results: example 1: image example 2: image

Hello, I also encountered the problem of not being able to visualize, could you please share your code? Hope to study together.

I am very sorry that I have not solved this problem. Moreover, you can try to use the script code eval_ycb.py in object-posenet to visualize the results after saving object predicting poses.

ethanshenze commented 1 year ago

@aragakiyui611 Hi, dear author:  When I want to reproduce the visualization results of BiCo-Net, I meet one problem. Could you mind helping me to solve this issue?(I also have sent an email to your mailbox).  I cannot get the right visualization results. Specially, the prediction point cloud cannot be visualized right while the target point cloud can do.

target_t = target_t + centroid
out_t = out_t + centroid
target = torch.bmm(model_points, target_r.transpose(2, 1)) + target_t # (1, 2620, 3)
pred = torch.bmm(model_points, out_R.transpose(2, 1)) + out_t # (1, 2620, 3)

Visualization results: example 1: image example 2: image

Hello, I also encountered the problem of not being able to visualize, could you please share your code? Hope to study together.

I am very sorry that I have not solved this problem. Moreover, you can try to use the script code eval_ycb.py in object-posenet to visualize the results after saving object predicting poses.

ok, thanks for your reply.