Comments (3)
In fact, the '2048' scales contain incomplete point cloud with numbers of [0,0,0] points. The model can fit any numbers of input points after training. At first, We want to translate those [0,0,0] points to generate the missing regions. But we have tested that those [0,0,0] points have no effect to the final results, so you can ignore them or just use this code. Thanks.
from pf-net-point-fractal-network.
Thank you very much!
And, you select 5 viewpoint. [1, 0, 1], [-1, 0, 0]...... What do these coordinates mean?
from pf-net-point-fractal-network.
Those points are the center of the missing regions. You can change the missing strategy yourself by modifing this part.(for example missing half of the point cloud)
from pf-net-point-fractal-network.
Related Issues (20)
- Test Dataset
- training
- What software do you use to draw the model structure diagram?
- 您好,请问可以提供一下point_netG0.pth文件吗?训练时间太长,目前没有合适的设备来完成训练过程,感谢! HOT 6
- How to Train on owner Datasets HOT 2
- 论文中4.2无监督 HOT 1
- collab example
- Questions about dense regions in the predictions
- Training with data captured from real scenarios HOT 1
- 消融实验
- Here are the trained weight files (modelG.pth) HOT 2
- Is the "errD" in "errD = errD_real + errD_fake" of Train_PFNet.py only used to print the loss value?
- Unable to access Model weights.
- 如果点云需要包含法向量,这个网络需要怎么改进呢
- No such file:'Checkpoint/point_netG.pth' HOT 2
- Can this be applied to plants HOT 1
- input
- Questions About Extracting Point Cloud Features Using 2D Convolution in the Code
- Test_csv infile HOT 3
- 关于损失函数chamfer_distance_numpy
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