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Hi!
Your work is really interesting :-) I'm doing my master thesis combining your method with other techniques and I have a small question about the implementation.
In your paper " Clustering of Static-Adaptive Correspondences for Deformable Object Tracking" , you propose to prefer static correspondences (obtained via global matching) over adaptive ones (derived from tracking with optical flow), but it seems that in the code you do the opposite when calling the member function "fusion.preferFirst()":
CMT.cpp, line 152
fusion.preferFirst(points_tracked, classes_tracked, points_matched_global, classes_matched_global,
points_fused, classes_fused);
The function first integrates all tracked points to the fused set and then adds each point from the global match set whose index does not coincide with any of the indices from the tracked points.
Do the paper and the implementation coincide? If so, then I misunderstood the algorithm > <
The text was updated successfully, but these errors were encountered:
Hi!
Your work is really interesting :-) I'm doing my master thesis combining your method with other techniques and I have a small question about the implementation.
In your paper " Clustering of Static-Adaptive Correspondences for Deformable Object Tracking" , you propose to prefer static correspondences (obtained via global matching) over adaptive ones (derived from tracking with optical flow), but it seems that in the code you do the opposite when calling the member function "fusion.preferFirst()":
CMT.cpp, line 152
fusion.preferFirst(points_tracked, classes_tracked, points_matched_global, classes_matched_global,
points_fused, classes_fused);
The function first integrates all tracked points to the fused set and then adds each point from the global match set whose index does not coincide with any of the indices from the tracked points.
Do the paper and the implementation coincide? If so, then I misunderstood the algorithm > <
The text was updated successfully, but these errors were encountered: