unmannedlab / lidar-reflectivity-segmentation Goto Github PK

Thank you for your impressive work.

I would like to ask some questions about your paper and code.

#1.
Using "data_generator.py", we can generate reflectivity data and i guess that code is based on Eq. (4) from your paper.
To utilize your code, i think the "eta_fit_grass.npy" file should be provided. How do we generate "eta_fit_grass.npy"? Also, is it effective to use grass-related data for a whole LiDAR point cloud which is mixed with different classes?

#2.
Furthermore, in "data_generator.py", ins[index] = ins[index]/p(rang[index])*1.8 is used and i'm wondering why "1.8" is used here.

#3.
To train alpha, we need to use "train_alpha.py" and 4 missing .npy files ("train_data_dotv2_test, train_GT_dotv2_test, val_data_dotv2_test, val_GT_dotv2_test") are required. Would you like to share those files? And is it possible to let me know how we generate those files just in case of a custom dataset?

Eager to hear from you soon.

Regards,
Inyoung Oh.

Thank you for sharing the nice work.
Unfortunately, I am having some trouble understanding the methodology. I was wondering if you have any other materials (slides, video explanation) available—many thanks.

Hi, thank you for your interesting work, I have some questions.

1. Since angels can be calculated as Intensity(R,rho,alpha)/maxIntensity(r,rho,alpha) at every range given points and labels, why we need an alpha_predictor to predict angels?
2. We can see 'p = np.poly1d(fit)' at line 99 in file 'data_generator.py', but parameter 'p' seems not being used in following code, the same as in 'data_generator_sem_poss.py' and 'data_generator_kitti.py'