3 ms·

Take a look at an older paper 'Content-Preserving Warps for 3D Video Stabilization'. That seems to do an ok job http://createdigitalmotion.com/2009/06/magical-3

by z303 12y ago

Take a look at an older paper 'Content-Preserving Warps for 3D Video Stabilization'. That seems to do an ok job http://createdigitalmotion.com/2009/06/magical-3d-warping-techniques-steadies-your-videos/ http://createdigitalmotion.com/2009/06/magical-3d-warping-te...

vanderZwan 12y ago
EDIT: After clicking through to their website, I found this updated technique which shares one principal author that seems to work even better: http://people.cs.nctu.edu.tw/~yushuen/VideoStabilization/ http://people.cs.nctu.edu.tw/~yushuen/VideoStabilization/ Wow. Actually, if they that add that technique to the mix it might solve the deformed "pop"-effect you see in some videos, like the deformed building you see around 16 seconds into this videos: http://research.microsoft.com/en-us/um/redmond/projects/hyperlapse/supplementary/html/bike1_original_speed.html http://research.microsoft.com/en-us/um/redmond/projects/hype...
- z303 12y ago
  Cool, I'd not seen the updated work. I wonder how much can be done in realtime. I have no idea what the compute split is between between the different processes. With sensors (gyros etc) the camera path would be trivial, instead of recovering that from the video. Rendering the results would be possible on a mobile GPU. Just leaving the frame conversion to a point cloud in terms of compute and memory. Maybe some scheme where you down sample the input frames to create the deformation mesh, then apply that to the full size frame would be the way to go
  - vanderZwan 12y ago
    > With sensors (gyros etc) the camera path would be trivial, instead of recovering that from the video. Well... not quite trivial. They're calibrated differently per model, and it's actually quite tricky to reconstruct the path based on accelerometers and gyroscope alone. There's also the likely issue of synchronising the data from these sensors with the video input. If you solve that second issue however, it could in theory at least help with recovering the path from the video, creating better predictions where the point cloud has moved to for example.
    - z303 12y ago
      I was being a little cheeky with that. I know position and orientation data can be quite noisy and getting clean good quality data is not simple

by z303 12y ago

vanderZwan 12y ago

EDIT: After clicking through to their website, I found this updated technique which shares one principal author that seems to work even better: http://people.cs.nctu.edu.tw/~yushuen/VideoStabilization/ http://people.cs.nctu.edu.tw/~yushuen/VideoStabilization/ Wow. Actually, if they that add that technique to the mix it might solve the deformed "pop"-effect you see in some videos, like the deformed building you see around 16 seconds into this videos: http://research.microsoft.com/en-us/um/redmond/projects/hyperlapse/supplementary/html/bike1_original_speed.html http://research.microsoft.com/en-us/um/redmond/projects/hype...

z303 12y ago
Cool, I'd not seen the updated work. I wonder how much can be done in realtime. I have no idea what the compute split is between between the different processes. With sensors (gyros etc) the camera path would be trivial, instead of recovering that from the video. Rendering the results would be possible on a mobile GPU. Just leaving the frame conversion to a point cloud in terms of compute and memory. Maybe some scheme where you down sample the input frames to create the deformation mesh, then apply that to the full size frame would be the way to go
- vanderZwan 12y ago
  > With sensors (gyros etc) the camera path would be trivial, instead of recovering that from the video. Well... not quite trivial. They're calibrated differently per model, and it's actually quite tricky to reconstruct the path based on accelerometers and gyroscope alone. There's also the likely issue of synchronising the data from these sensors with the video input. If you solve that second issue however, it could in theory at least help with recovering the path from the video, creating better predictions where the point cloud has moved to for example.
  - z303 12y ago
    I was being a little cheeky with that. I know position and orientation data can be quite noisy and getting clean good quality data is not simple