Piero V.

On acquiring 3D models of people

For my Master’s degree thesis, I dove into acquiring static objects with a RGBD sensor. Eventually, I decided to use the Kinect Fusion algorithm, which produced decent results.

I found this topic fascinating, so I continued on my own, but in another direction: acquiring people. So, in the last few months, I have been experimenting with scanning myself and my friends with a Microsoft Kinect One.

Conditioned by my previous results, initially, I tried with point clouds.

Deformation graph approach

One approach I found in several papers consists in:

  1. acquiring only a few scans (from 6-8 points of view), with the person as still as they can;
  2. performing a rough global alignment;
  3. running ICP to improve the rigid alignment locally;
  4. downsampling the point cloud to build a deformation graph;
  5. resolving an optimization problem;
  6. deforming the denser point clouds.

Reaching point 3 is not trivial because people move. ICP can be very unforgiving, and in some cases, you also need some luck to obtain good results at this stage.

For downsampling (Point 4), I used Open3D’s voxelization followed by averaging point coordinates. I do not know how this can influence the final results compared to something like a clustering algorithm. … [Leggi il resto]