Loss functions for regularization
In the previous section, we successfully formulated the deformable mesh fitting problem into an optimization problem. However, the approach of directly optimizing this primary loss function can be problematic. The issues lie in that there may exist multiple mesh models that can be good fits to the same point cloud. These mesh models that are good fits may include some mesh models that are far away from smooth meshes.
On the other hand, we usually have prior knowledge about pedestrians. For example, the surfaces of pedestrians are usually smooth, the surface norms are smooth also. Thus, even if a non-smooth mesh is close to the input point cloud in terms of Chamfer distance, we know with a certain level of confidence that it is far away from the ground truth.
Machine learning literature has provided solutions for excluding such undesirable non-smooth solutions for several decades. The solution is called regularization. Essentially, the loss we...
