A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation: Supplementary Material
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چکیده
• For the second pass (3DPost−1), we revert time to the previous frame t−1 and save all vertices’ 3D positions at that time. We then return to the current frame t and use the vertex 3D positions at time t to project the 3D vertices of time t − 1 into image space. Hence, we again store 3D positions for each pixel, but this time the 3D positions from time t−1 using the projection at time t. Frame t−1 Frame t Frame t+1
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