  Rendering realistic images from 3D reconstruction is an essential task of
many Computer Vision and Robotics pipelines, notably for mixed-reality
applications as well as training autonomous agents in simulated environments.
However, the quality of novel views heavily depends of the source
reconstruction which is often imperfect due to noisy or missing geometry and
appearance. Inspired by the recent success of reference-based super-resolution
networks, we propose MaRINeR, a refinement method that leverages information of
a nearby mapping image to improve the rendering of a target viewpoint. We first
establish matches between the raw rendered image of the scene geometry from the
target viewpoint and the nearby reference based on deep features, followed by
hierarchical detail transfer. We show improved renderings in quantitative
metrics and qualitative examples from both explicit and implicit scene
representations. We further employ our method on the downstream tasks of
pseudo-ground-truth validation, synthetic data enhancement and detail recovery
for renderings of reduced 3D reconstructions.
