Let’s take a look at some results. Our method is the first (along with concurrent work) to tackle this problem with unsupervised learning: it has never seen any ground truth decomposition at train time.
Our goal is thus to approach the quality of results of the supervised methods, trained with knowledge of ground truth decompositions.
This slide shows a synthetic scene taken from [Bonneel et al., 17] (left column), which comes with known decompositions (we did not use for training, as opposed to the other works). Bonneel et al. state in their report that the problem is unsolved: no result is fully satisfying.
Our work (2nd column) is compared against [Zhou et al., 15] (a state of the art supervised method, 3rd column) and [Zhao et al., 15] (a state of the art human-devised optimization method, right column).
One can observe several things.
First, it is hard to say one method is clearly better than another. They differ in different ways, and none fully solves the problem.
On the plus-side for our method, we can notice that:
- GT shading is colored, as is ours, unlike related work,
- GT albedo is textured, as is ours (slightly), whereas related work relies on an assumption of sparse and piecewise constant albedo,
- The flaws of our method are also present in the other methods’ results: shadows in the albedo, textures spilling in the shading.
On the down-side for any algorithm:
- None fully solve the problem,
- Texture spilling in the shading and vice versa.