  Semantic scene understanding is an essential task for self-driving vehicles
and mobile robots. In our work, we aim to estimate a semantic segmentation map,
in which the foreground objects are removed and semantically inpainted with
background classes, from a single RGB image. This semantic foreground
inpainting task is performed by a single-stage convolutional neural network
(CNN) that contains our novel max-pooling as inpainting (MPI) module, which is
trained with weak supervision, i.e., it does not require manual background
annotations for the foreground regions to be inpainted. Our approach is
inherently more efficient than the previous two-stage state-of-the-art method,
and outperforms it by a margin of 3% IoU for the inpainted foreground regions
on Cityscapes. The performance margin increases to 6% IoU, when tested on the
unseen KITTI dataset. The code and the manually annotated datasets for testing
are shared with the research community at
https://github.com/Chenyang-Lu/semantic-foreground-inpainting.
