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.

通过反向程序化建模的方法，我们提出了一种三维重建技术，并研究了两种变种。第一种选择使用遗传算法来拟合输入参数的集合，我们展示了我们在树模型和复杂对象的实验结果。第二种选择通过使用变异算法、可微分渲染和不可微分程序化生成器，显著提高了精度。我们的研究有两个主要贡献：首先，我们提出了一种将可微分渲染和反向程序化建模相结合的方法，从而在只有少量输入图像可用时（甚至只有一张图像），比现有方法更精确地重建三维模型；其次，我们将可微分和不可微分的程序化生成器结合在一个框架中，允许我们将反向程序化建模应用于相当复杂的生成器：当梯度可用时，重建是精确的；当梯度不可用时，重建是近似的，但总是高质量且无视觉伪影。