PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN

Publikation

Yaxu Xie; Jason Raphael Rambach; Fangwen Shu; Didier Stricker

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.

Projekte

Moveon - Visuelles robustes räumliches Szenenverständnis in dynamischen Umgebungen unter Verwendung von intermediären Darstellungen,
RACKET - Lernen seltener Klassen und Erkennung unbekannter Ereignisse in der flexiblen Produktion

ICRA_2021___PlaneSegNet.pdf (pdf, 9 MB )

PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN

Zusammenfassung

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