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gengshan-y avatar gengshan-y commented on May 18, 2024

Hi Xianghui, the flatten loss is borrowed from SoftRas. See the explanation here.

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xiexh20 avatar xiexh20 commented on May 18, 2024

Thank you so much for your explanation.

I have another question about the principal points prediction and processing part:

lasr/nnutils/mesh_net.py

Lines 209 to 217 in 29d8759

# transform the CNN-predicted principal points
# (wrt original image, before cropping) to the cropped image
ppb1 = self.cams[:local_batch_size,:1]*self.pp[:local_batch_size]/(opts.img_size/2.)
ppb2 = self.cams[local_batch_size:,:1]*self.pp[local_batch_size:]/(opts.img_size/2.)
# represent pp of croped frame 2 as transformed pp of cropped frame 1
# to reduce ambiguity caused by inconsistent pp over time
ppa1 = ppoint[:local_batch_size] + ppb1 + 1
ppa2 = ppa1 * (self.cams[local_batch_size:,:1] / self.cams[:local_batch_size,:1])
ppoint[local_batch_size:]= ppa2 - ppb2 -1

I understand that the first two lines normalize the top left corners of the cropped patch, but why is it necessary to compute ppoints of the next frame from current frame?

Also in your paper you said it is assumed the principle points are in the center of the image, why it is still needed here to predict the ppoints? Can we use zeros for this?

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gengshan-y avatar gengshan-y commented on May 18, 2024

why is it necessary to compute ppoints of the next frame from current frame?

We found using a constant principal points for a given frame pair (when computing the flow loss) produces better camera poses than predicting two separate pps. A reason is that separate pps are too flexible, which allows the change over pps to explain the majority of observed flow (instead of explaining by camera motion).

Also in your paper you said it is assumed the principle points are in the center of the image, why it is still needed here to predict the ppoints? Can we use zeros for this?

We could use a constant pp. But in practice that causes non-overlapping rendering issue in certain cases: see gengshan-y/viser#5

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xiexh20 avatar xiexh20 commented on May 18, 2024

I see, thank you so much for your quick response!

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