You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
How to set the disparity range from two ends and how to use 'ad' and 'census' to generate disparity map directly without using the constructed network?
#40
Anyone knows: How to set the disparity range from two ends? e.g. if we want to search from 100 to 300 as (100, 300) to find the suitable disparity for each pixel, how can we give this range to the code? Now, only 'disp_max' is used to make the searching disparity range as (0, disp_max)!
Also how to just use 'ad' and 'census' to generate disparity map directly without using the neural network, as the author did in his paper to compare the results of the constructed neural network with normal dense matching algorithms?
Any help is appreciated!
The text was updated successfully, but these errors were encountered:
I have the same question, I just want to use 'ad' to generate disparity. I try the command parameters like this ./main.lua kitti ad-a predict -net_fname net/net_kitti_fast_-a_train_all.t7 -left samples/input/kittiL.png -right samples/input/kittiR.png -disp_max 70 -sm_terminate cnn. But it seems not to work.
maybe you should try:
./main.lua kitti ad -a predict -left samples/input/kittiL.png -right
samples/input/kittiR.png -disp_max 70 -sm_terminate cnn.
add me via qq (1376519063), we can talk further!
Best regards,
2017-11-27 14:38 GMT+01:00 Xianshun Wang <[email protected]>:
I have the same question, I just want to use 'ad' to generate disparity. I
try to the command parameters like this ./main.lua kitti ad-a predict
-net_fname net/net_kitti_fast_-a_train_all.t7 -left
samples/input/kittiL.png -right samples/input/kittiR.png -disp_max 70
-sm_terminate cnn. But it seems not to work.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub
<#40 (comment)>, or mute
the thread
<https://github.com/notifications/unsubscribe-auth/Ac_y0qSPbYSf2_glcZN3TZFI9LPXVwGsks5s6rtOgaJpZM4PJDZ3>
.
You may manipulate the source code which requires knowledge in cuda and torch or you may use the produced left/right volumes which is easier. Note that you have 3 outputs: left.bin, right.bin, and disp.bin. Two first files are cost volumes.
Hi everyone,
Anyone knows: How to set the disparity range from two ends? e.g. if we want to search from 100 to 300 as (100, 300) to find the suitable disparity for each pixel, how can we give this range to the code? Now, only 'disp_max' is used to make the searching disparity range as (0, disp_max)!
Also how to just use 'ad' and 'census' to generate disparity map directly without using the neural network, as the author did in his paper to compare the results of the constructed neural network with normal dense matching algorithms?
Any help is appreciated!
The text was updated successfully, but these errors were encountered: