front_x or cross_x?

[sunnyHelen]

https://github.com/JonDoe-297/cross-view/blob/957691b0f00bafb59d1fc7146e6408180f3b7649/crossView/CrossViewTransformer.py#L52 https://github.com/JonDoe-297/cross-view/blob/957691b0f00bafb59d1fc7146e6408180f3b7649/crossView/CrossViewTransformer.py#L55 Hi, I was confused about the calculation of cross-view transformer. According to the Figure 4 in your paper, it seems that the "front_x" should be "cross_x" in Line 52 and 55. Am I misunderstanding anything? Please help me figure it out. Looking forward to your reply and thanks!

[ChristopheZhao]

I have test the difference between front_x and cross_x that concanate with T: sample 1(cross_x is better): origin front: the res used front_x: the res used cross_x:

sample 2(cross_x is better): origin front: the res used front_x: the res used corss_x:

sample 3(front_x is better): the res used front_x: the res used cross_x:

And more 10 sample i tested has same degree result that i cant distinguishing their difference,so i confused if the pretrain model used front_x concanated T to train data.But maybe the right ways is to concanated with cross_x,So although use cross_x concanated with T to test sample,also can achive the same result as font_x.