Comments (5)
Thanks for your interest.
Unfortunately, we didn't have such a comparison. However, we anticipate the performance will be not that good, as the resolution of estimated disparity without refinement is relatively low and the residual learning (i.e., refinement) scheme has been proved to be effective in many previous works (e.g., StereoNet). You can train with our code to see what happens.
Sorry about that we have no plan to extend the deformable conv to 3D currently.
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Thanks
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Hello, @haofeixu
As I know, the scale_factor
is the ratio of left_img Height and low_disp Height.
But, why the disp
need to multiply the scale_factor
?
Lines 45 to 47 in 5e2359c
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Hi @Todd-Qi ,
As the low resolution disparity is upsampled to higher resolution, it's magnitude needs to be scaled correspondingly.
That is, a 1-pixel disparity at 1/4 resolution corresponds to 2-pixel for 1/2 resolution.
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Thanks for your clear explanation.
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Related Issues (20)
- Directory issue HOT 1
- error in modulated_deformable_im2col_cuda: invalid device function HOT 2
- how to set max_disp higher than default? HOT 3
- Run in Google colab HOT 4
- error in modulated_deformable_im2col_cuda: no kernel image is available for execution on the device HOT 1
- CUDA error: unknown error HOT 4
- question HOT 1
- Getting negative archetypal coordinates HOT 4
- RuntimeError: CUDA error: an illegal memory access was encountered HOT 1
- can you share your training log HOT 2
- question HOT 1
- visualize the sampling points, HOT 1
- where is the groundtruth file disp_occ_0_pseudo_gt HOT 1
- Undefined Symbol Error, most likely because i am using CUDA 11 instead of the intended CUDA 10 HOT 1
- how to set input size HOT 3
- evaluating indicator and Ablation study HOT 1
- CSCA--Cross Scale Cost Aggregation HOT 2
- Predict Disparity Map
- why we get the wrong demo HOT 1
- some question about import error HOT 1
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