TOSN-Trans：Transparent Object Segmentation Network with Transformer

Authors: Tao Tao
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: RGB-D, glass segmentation, feature fusion, dual-network, efficient transformer, boundary optimization

Abstract:
Due to the optical properties of glass materials, most glass appears transparent in RGB images. However, in depth images, different acquisition methods make glass visible. Therefore, Therefore, using RGB-D dual-channel feature input makes it easier to recognize and segment glass objects. Building on this concept, we propose a multi-layer symmetrical dual-channel network architecture, which can effectively realize trans-modal feature fusion of RGB-D images based on attention mechanism, and integrate Convolutional and Transformer architectures to extract local features and non-local dependencies, respectively. To further enhance segmentation accuracy and efficiency, this paper also designs a boundary optimization module. This module constructs a distance map based on edge prediction guidance, enabling high-precision glass edge recognition. To support this work, we collect a new dataset comprising 5551 sets of calibrated RGB-D images. The effectiveness and accuracy of the proposed glass segmentation method are rigorously evaluated quantitatively and qualitatively. The code for this paper has been published at:https://github.com/Jaccury/RGB-D-Transparent-object-segmentation.

Decision: desk reject

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