  We propose a method at the intersection of Computer Vision and Computer
Graphics fields, which automatically generates RGBD images using neural
networks, based on previously seen and synchronized video, depth and pose
signals. Since the models must be able to reconstruct both texture (RGB) and
structure (Depth), it creates an implicit representation of the scene, as
opposed to explicit ones, such as meshes or point clouds. The process can be
thought of as neural rendering, where we obtain a function f : Pose -> RGBD,
which we can use to navigate through the generated scene, similarly to graphics
simulations. We introduce two new datasets, one based on synthetic data with
full ground truth information, while the other one being recorded from a drone
flight in an university campus, using only video and GPS signals. Finally, we
propose a fully unsupervised method of generating datasets from videos alone,
in order to train the Pose2RGBD networks. Code and datasets are available at::
https://gitlab.com/mihaicristianpirvu/pose2rgbd.
