Related papers: Glass Surface Segmentation with an RGB-D Camera via Weighted Feature Fusion for Service Robots

Glass Surface Segmentation with an RGB-D Camera via Weighted Feature Fusion for Service Robots

URL: http://arxiv.org/abs/2508.01639v1
Date: Sun, 03 Aug 2025 07:58:10 GMT
Title: Glass Surface Segmentation with an RGB-D Camera via Weighted Feature Fusion for Service Robots
Authors: Henghong Lin, Zihan Zhu, Tao Wang, Anastasia Ioannou, Yuanshui Huang,
Abstract summary: We propose a Weighted Feature Fusion (WFF) module that combines RGB and depth features to tackle issues such as transparency and reflections.<n>We also introduce the MJU-Glass dataset, a comprehensive RGB-D dataset collected by a service robot navigating real-world environments.
Score: 4.58103945996187
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We address the problem of glass surface segmentation with an RGB-D camera, with a focus on effectively fusing RGB and depth information. To this end, we propose a Weighted Feature Fusion (WFF) module that dynamically and adaptively combines RGB and depth features to tackle issues such as transparency, reflections, and occlusions. This module can be seamlessly integrated with various deep neural network backbones as a plug-and-play solution. Additionally, we introduce the MJU-Glass dataset, a comprehensive RGB-D dataset collected by a service robot navigating real-world environments, providing a valuable benchmark for evaluating segmentation models. Experimental results show significant improvements in segmentation accuracy and robustness, with the WFF module enhancing performance in both mean Intersection over Union (mIoU) and boundary IoU (bIoU), achieving a 7.49% improvement in bIoU when integrated with PSPNet. The proposed module and dataset provide a robust framework for advancing glass surface segmentation in robotics and reducing the risk of collisions with glass objects.

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