  Dynamic objects have a significant impact on the robot's perception of the
environment which degrades the performance of essential tasks such as
localization and mapping. In this work, we address this problem by synthesizing
plausible color, texture and geometry in regions occluded by dynamic objects.
We propose the novel geometry-aware DynaFill architecture that follows a
coarse-to-fine topology and incorporates our gated recurrent feedback mechanism
to adaptively fuse information from previous timesteps. We optimize our
architecture using adversarial training to synthesize fine realistic textures
which enables it to hallucinate color and depth structure in occluded regions
online in a spatially and temporally coherent manner, without relying on future
frame information. Casting our inpainting problem as an image-to-image
translation task, our model also corrects regions correlated with the presence
of dynamic objects in the scene, such as shadows or reflections. We introduce a
large-scale hyperrealistic dataset with RGB-D images, semantic segmentation
labels, camera poses as well as groundtruth RGB-D information of occluded
regions. Extensive quantitative and qualitative evaluations show that our
approach achieves state-of-the-art performance, even in challenging weather
conditions. Furthermore, we present results for retrieval-based visual
localization with the synthesized images that demonstrate the utility of our
approach.
