Generalizable 3D Scene Reconstruction via Divide and Conquer from a
  Single View
Andreea Dogaru Mert Özer Bernhard Egger
Single-view 3D reconstruction is currently approached from two dominant
perspectives: reconstruction of scenes with limited diversity using 3D data
supervision or reconstruction of diverse singular objects using large image
priors. However, real-world scenarios are far more complex and exceed the
capabilities of these methods. We therefore propose a hybrid method following a
divide-and-conquer strategy. We first process the scene holistically,
extracting depth and semantic information, and then leverage a single-shot
object-level method for the detailed reconstruction of individual components.
By following a compositional processing approach, the overall framework
achieves full reconstruction of complex 3D scenes from a single image. We
purposely design our pipeline to be highly modular by carefully integrating
specific procedures for each processing step, without requiring an end-to-end
training of the whole system. This enables the pipeline to naturally improve as
future methods can replace the individual modules. We demonstrate the
reconstruction performance of our approach on both synthetic and real-world
scenes, comparing favorable against prior works. Project page:
https://andreeadogaru.github.io/Gen3DSR.