  Recent advances in the area of plane segmentation from single RGB images show
strong accuracy improvements and now allow a reliable segmentation of indoor
scenes into planes. Nonetheless, fine-grained details of these segmentation
masks are still lacking accuracy, thus restricting the usability of such
techniques on a larger scale in numerous applications, such as inpainting for
Augmented Reality use cases. We propose a post-processing algorithm to align
the segmented plane masks with edges detected in the image. This allows us to
increase the accuracy of state-of-the-art approaches, while limiting ourselves
to cuboid-shaped objects. Our approach is motivated by logistics, where this
assumption is valid and refined planes can be used to perform robust object
detection without the need for supervised learning. Results for two baselines
and our approach are reported on our own dataset, which we made publicly
available. The results show a consistent improvement over the state-of-the-art.
The influence of the prior segmentation and the edge detection is investigated
and finally, areas for future research are proposed.
