Single-view 3D reconstruction via inverse procedural modeling
Albert Garifullin Nikolay Maiorov Vladimir Frolov
We propose an approach to 3D reconstruction via inverse procedural modeling
and investigate two variants of this approach. The first option consists in the
fitting set of input parameters using a genetic algorithm. We demonstrate the
results of our work on tree models, complex objects, with the reconstruction of
which most existing methods cannot handle. The second option allows us to
significantly improve the precision by using gradients within memetic
algorithm, differentiable rendering and also differentiable procedural
generators. In our work we see 2 main contributions. First, we propose a method
to join differentiable rendering and inverse procedural modeling. This gives us
an opportunity to reconstruct 3D model more accurately than existing approaches
when a small number of input images are available (even for single image).
Second, we join both differentiable and non-differentiable procedural
generators in a single framework which allow us to apply inverse procedural
modeling to fairly complex generators: when gradient is available,
reconstructions is precise, when gradient is not available, reconstruction is
approximate, but always high quality without visual artifacts.