In: IEEE International Conference on Robotics and Automation. IEEE International Conference on Robotics and Automation (ICRA-2021), May 30 - June 5, Xi'an, China, IEEE, 2021.
Zusammenfassung
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 par- ticularity 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.
@inproceedings{pub11532,
author = {
Xie, Yaxu
and
Rambach, Jason Raphael
and
Shu, Fangwen
and
Stricker, Didier
},
title = {PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN},
booktitle = {IEEE International Conference on Robotics and Automation. IEEE International Conference on Robotics and Automation (ICRA-2021), May 30-June 5, Xi'an, China},
year = {2021},
publisher = {IEEE}
}
Deutsches Forschungszentrum für Künstliche Intelligenz German Research Center for Artificial Intelligence