  Computer vision tasks are often expected to be executed on compressed images.
Classical image compression standards like JPEG 2000 are widely used. However,
they do not account for the specific end-task at hand. Motivated by works on
recurrent neural network (RNN)-based image compression and three-dimensional
(3D) reconstruction, we propose unified network architectures to solve both
tasks jointly. These joint models provide image compression tailored for the
specific task of 3D reconstruction. Images compressed by our proposed models,
yield 3D reconstruction performance superior as compared to using JPEG 2000
compression. Our models significantly extend the range of compression rates for
which 3D reconstruction is possible. We also show that this can be done highly
efficiently at almost no additional cost to obtain compression on top of the
computation already required for performing the 3D reconstruction task.
