Explanation of LidarBoost Algorithm? I am trying to understand the LidarBoost algorithm as explained in this paper (PDF warning). 

I don't understand how they take the original depth-images $Y_k$ and transform them into the up-sampled images $D_k$. I get how optical flow is used to align the $Y_k$ into a chosen reference frame, but is the transform between $Y_k$ and $D_k$ just a standard image up-sampling with nearest neighbors for the "interpolation" step? If that's the case, can someone explain how they get the term $W_k$ in their data term of the energy function? They say this about it, but I don't understand how to construct $W_k$ for each $k$:


  $W_k \in \mathbb{R}^{\beta_n \times \beta_m}$ is a banded matrix that
  encodes the positions of $D_k$ which one samples from during
  resampling on the high-resolution target grid


I'm trying (so far frustratingly ineffectively) to get enough of a sense of the algorithm to try to implement it.
It sounds like $W_k$ is just a matrix of 1's and 0's.  When $W_k$ is a 1, then the corresponding range value in $D_k$ is used. If it's a 0, then that range value is not used.

Similarly, $T_k$ should be mostly 1, and 0 when the corresponding $D_k$ values are unreliable.

Does that tally with your understanding?  If not, can you elaborate on your question with more details of what you suspect?