Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage
  Instance Segmentation CNN   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.

29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph