  Piece-wise planar 3D reconstruction simultaneously segments plane instances
and recovers their 3D plane parameters from an image, which is particularly
useful for indoor or man-made environments. Efficient reconstruction of 3D
planes coupled with semantic predictions offers advantages for a wide range of
applications requiring scene understanding and concurrent spatial mapping.
However, most existing planar reconstruction models either neglect semantic
predictions or do not run efficiently enough for real-time applications. We
introduce SOLOPlanes, a real-time planar reconstruction model based on a
modified instance segmentation architecture which simultaneously predicts
semantics for each plane instance, along with plane parameters and piece-wise
plane instance masks. We achieve an improvement in instance mask segmentation
by including multi-view guidance for plane predictions in the training process.
This cross-task improvement, training for plane prediction but improving the
mask segmentation, is due to the nature of feature sharing in multi-task
learning. Our model simultaneously predicts semantics using single images at
inference time, while achieving real-time predictions at 43 FPS.
