  In the last few years, there has been a growing interest in taking advantage
of the 360 panoramic images potential, while managing the new challenges they
imply. While several tasks have been improved thanks to the contextual
information these images offer, object recognition in indoor scenes still
remains a challenging problem that has not been deeply investigated. This paper
provides an object recognition system that performs object detection and
semantic segmentation tasks by using a deep learning model adapted to match the
nature of equirectangular images. From these results, instance segmentation
masks are recovered, refined and transformed into 3D bounding boxes that are
placed into the 3D model of the room. Quantitative and qualitative results
support that our method outperforms the state of the art by a large margin and
show a complete understanding of the main objects in indoor scenes.
