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What's the meaninig of 0.625, 1.25, 2.5, 5? Is there any geometry motivation?
I think the factors should be 2, because when you upsample a flow to a resolution with double height and width, the flow is double due to double pixels between origin points and corresponding points.
The text was updated successfully, but these errors were encountered:
Please note that each decoder of FastFlowNet estimates optical flow whose magnitude is 1/20 of ground truth flow, therefore, to convert to true displacement before warping, we should multiply a scale factor of 20/(2^l), where l belongs to {2, 3, 4, 5} means the pyramid level. We adopt the same scaling approach as PWC-Net and LiteFlowNet.
There are some magic factors when upsample flow to higher resolution:
https://github.com/ltkong218/FastFlowNet/blob/main/models/FastFlowNet.py#L140
https://github.com/ltkong218/FastFlowNet/blob/main/models/FastFlowNet.py#L147
https://github.com/ltkong218/FastFlowNet/blob/main/models/FastFlowNet.py#L154
https://github.com/ltkong218/FastFlowNet/blob/main/models/FastFlowNet.py#L161
What's the meaninig of 0.625, 1.25, 2.5, 5? Is there any geometry motivation?
I think the factors should be 2, because when you upsample a flow to a resolution with double height and width, the flow is double due to double pixels between origin points and corresponding points.
The text was updated successfully, but these errors were encountered: