  This paper is about reducing the cost of building good large-scale 3D
reconstructions post-hoc. We render 2D views of an existing reconstruction and
train a convolutional neural network (CNN) that refines inverse-depth to match
a higher-quality reconstruction. Since the views that we correct are rendered
from the same reconstruction, they share the same geometry, so overlapping
views complement each other. We take advantage of that in two ways. Firstly, we
impose a loss during training which guides predictions on neighbouring views to
have the same geometry and has been shown to improve performance. Secondly, in
contrast to previous work, which corrects each view independently, we also make
predictions on sets of neighbouring views jointly. This is achieved by warping
feature maps between views and thus bypassing memory-intensive 3D computation.
We make the observation that features in the feature maps are
viewpoint-dependent, and propose a method for transforming features with
dynamic filters generated by a multi-layer perceptron from the relative poses
between views. In our experiments we show that this last step is necessary for
successfully fusing feature maps between views.
