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

• URL: http://arxiv.org/abs/2103.15428v1
• Date: Mon, 29 Mar 2021 08:53:05 GMT
• Title: PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN
• Authors: Yaxu Xie, Jason Rambach, Fangwen Shu, Didier Stricker
• Abstract summary: We propose a real-time deep neural architecture that estimates piece-wise planar regions from a single RGB image. Our method achieves significantly higher frame-rates and comparable segmentation accuracy against two-stage methods.
• Score: 12.251947429149796
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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