Open narutojxl opened 4 years ago
Since the direction in 3D space have two directions, for convenience, we choose the direction with its x value alway positive. In addition, we have mentioned this point in our preprint paper:)
"we choose the direction with positive X components, i.e., Cd = sign(C{d_x}) · C_d."
The compute of covariance is correct, which is more effective that can save more time in computation.
Hi @ziv-lin , thanks for your reply!
For covariance calculate, i don't understand why paper equ3. equal to the format in code, could you please give some tips?
For calculate histogram of keyframe, you say "we choose the direction with positive X components, i.e., Cd = sign(C{d_x}) · C_d.". I understand what you mean is for negative x component, we should ignore these cells, we only use positive x of cells to calculate histogram. Did I get it wrong?
For center i see the code also slightly different from the paper equ1.
There is a point p(black) in the bound of the following picture, which constructed with the center point(red) and block size. I found that some black points doesn't belong to the cell, because they created cell's center does not equal to the red one. Is that right? The correspongding code is find_cell().
Hi @ziv-lin , thanks for sharing your work to community. when calculating cell's center cell_map_keyframe.hpp func find_cell_center() and covariance func get_covmat() in code is as follows, different from the paper equ1. and equ3. repectively, does this matter?
When calculating feature direction in func feature_direction(), in paper V-C(2D histogram of keyframe), " we choose the direction with positive X components", in code we transform points whose x part is negative into positive, could you please explain it? Looking forward to your reply, thanks for your help!