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360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume: Difference between revisions
360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume (view source)
Revision as of 12:55, 16 June 2020
, 16 June 2020→Learnable Cost Volume
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Each slice \(i\) is computed by taking images \(I_1\) and \(I_2\), sliding image \(I_2\) down by \(i\) pixels, and subtracting them to yield | Each slice \(i\) is computed by taking images \(I_1\) and \(I_2\), sliding image \(I_2\) down by \(i\) pixels, and subtracting them to yield | ||
<pre> | <pre> | ||
# This is not their actual code. Their actual code is slightly more complicated. | |||
cost_volume[:,:,i] = I_1 - stack(tile(I_2[:1,:], [i,1]), I_2[i:,:], axis=0) | cost_volume[:,:,i] = I_1 - stack(tile(I_2[:1,:], [i,1]), I_2[i:,:], axis=0) | ||
</pre> | </pre> | ||
They learn the optimal step sizes by applying a 7x1 Conv2D CNN. | They learn the optimal step sizes by applying a 7x1 Conv2D CNN. | ||
{{ hidden | Their cost volume code | | |||
<syntaxhighlight lang=python> | |||
cost = Variable(torch.FloatTensor(refimg_fea.size()[0], refimg_fea.size()[1]*2, self.maxdisp/4 *3, refimg_fea.size()[2], refimg_fea.size()[3]).zero_()).cuda() | |||
for i in range(self.maxdisp/4 *3): | |||
if i > 0 : | |||
cost[:, :refimg_fea.size()[1], i, :,:] = refimg_fea[:,:,:,:] | |||
cost[:, refimg_fea.size()[1]:, i, :,:] = shift_down[:,:,:,:] | |||
shift_down = self.forF(shift_down) | |||
else: | |||
cost[:, :refimg_fea.size()[1], i, :,:] = refimg_fea | |||
cost[:, refimg_fea.size()[1]:, i, :,:] = targetimg_fea | |||
shift_down = self.forF(targetimg_fea) | |||
cost = cost.contiguous() | |||
</syntaxhighlight> | |||
===3D Encoder-Decoder=== | ===3D Encoder-Decoder=== | ||