Digging Into Self-Supervised Monocular Depth Estimation: Difference between revisions
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===Per-Pixel Minimum Reprojection Loss=== | ===Per-Pixel Minimum Reprojection Loss=== | ||
Basically you have two images in a sequence: frame1, frame2, frame3. | |||
Each gives you a loss: | |||
<pre> | |||
loss1 = abs(frame2 - warp(frame1)) | |||
loss2 = abs(frame2 - warp(frame3)) | |||
# Take the minimum over all pixels | |||
loss = mean(min(loss1, loss2)) | |||
</pre> | |||
===Auto-Masking Stationary Pixels=== | ===Auto-Masking Stationary Pixels=== | ||
===Multi-scale Estimation=== | ===Multi-scale Estimation=== | ||
Revision as of 16:58, 10 July 2020
Digging Into Self-Supervised Monocular Depth Estimation (ICCV 2019)
Monodepth2
Authors: Clement Godard, Oisin Mac Aodha, Michael Firman, Gabriel Brostow Affiliations: UCL, Caltech, Niantic
Method
They perform self-supervised training by using the depth for view-synthesis and comparing to other images.
Given a source view \(I_{t'}\) and a target view \(I_t\), let the following:
- \(T_{t \to t'}\) be the relative pose of \(t'\)
- \(D_t\) the depth map of view \(t\)
- \(L_p = \sum_{t'}pe(I, I_{t' \to t}\) the cumulative reprojection error
- \(I_{t' \to t} = I_{t'}\langle proj(D_t, T_{t \to t'}, K)\rangle \) the projection
From this layout, they make the following contributions:
Per-Pixel Minimum Reprojection Loss
Basically you have two images in a sequence: frame1, frame2, frame3.
Each gives you a loss:
loss1 = abs(frame2 - warp(frame1)) loss2 = abs(frame2 - warp(frame3)) # Take the minimum over all pixels loss = mean(min(loss1, loss2))