  Realistic color texture generation is an important step in RGB-D surface
reconstruction, but remains challenging in practice due to inaccuracies in
reconstructed geometry, misaligned camera poses, and view-dependent imaging
artifacts.
  In this work, we present a novel approach for color texture generation using
a conditional adversarial loss obtained from weakly-supervised views.
  Specifically, we propose an approach to produce photorealistic textures for
approximate surfaces, even from misaligned images, by learning an objective
function that is robust to these errors.
  The key idea of our approach is to learn a patch-based conditional
discriminator which guides the texture optimization to be tolerant to
misalignments.
  Our discriminator takes a synthesized view and a real image, and evaluates
whether the synthesized one is realistic, under a broadened definition of
realism.
  We train the discriminator by providing as `real' examples pairs of input
views and their misaligned versions -- so that the learned adversarial loss
will tolerate errors from the scans.
  Experiments on synthetic and real data under quantitative or qualitative
evaluation demonstrate the advantage of our approach in comparison to state of
the art. Our code is publicly available with video demonstration.
