Paper accepted at ICRA 2021

We are delighted to announce that our paper PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN has been accepted for publication at the ICRA 2021 IEEE International Conference on Robotics and Automation which will take place from May 30 to June 5, 2021 at Xi’an, China.

Abstract: 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.

Authors: Yaxu Xie, Jason Raphael Rambach, Fangwen Shu, Didier Stricker

Paper: https://av.dfki.de/publications/planesegnet-fast-and-robust-plane-estimation-using-a-single-stage-instance-segmentation-cnn/

Contact: Yaxu.Xie@dfki.de, Jason.Rambach@dfki.de

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