  Instance segmentation of planar regions in indoor scenes benefits visual SLAM
and other applications such as augmented reality (AR) where scene understanding
is required. Existing methods built upon two-stage frameworks show satisfactory
accuracy but are limited by low frame rates. In this work, we propose a
real-time deep neural architecture that estimates piece-wise planar regions
from a single RGB image. Our model employs a variant of a fast single-stage CNN
architecture to segment plane instances. Considering the particularity of the
target detected, we propose Fast Feature Non-maximum Suppression (FF-NMS) to
reduce the suppression errors resulted from overlapping bounding boxes of
planes. We also utilize a Residual Feature Augmentation module in the Feature
Pyramid Network (FPN). Our method achieves significantly higher frame-rates and
comparable segmentation accuracy against two-stage methods. We automatically
label over 70,000 images as ground truth from the Stanford 2D-3D-Semantics
dataset. Moreover, we incorporate our method with a state-of-the-art planar
SLAM and validate its benefits.
