Abstract
Glass is a prevalent material among solid objects in everyday life, yet segmentation methods struggle to distinguish it from opaque materials due to its transparency and reflection. While it is known that human perception relies on boundary and reflective-object features to distinguish glass objects, the existing literature has not yet sufficiently captured both properties when handling transparent objects. Hence, we propose incorporating both of these powerful visual cues via the Boundary Feature Enhancement and Reflection Feature Enhancement modules in a mutually beneficial way. Our proposed framework, TransCues, is a pyramidal transformer encoder-decoder architecture to segment transparent objects. We empirically show that these two modules can be used together effectively, improving overall performance across various benchmark datasets, including glass object semantic segmentation, mirror object semantic segmentation, and generic segmentation datasets. Our method outperforms the state-of-the-art by a large margin, achieving +4.2% mIoU on Trans10K-v2, +5.6% mIoU on MSD, +10.1% mIoU on RGBD-Mirror, +13.1% mIoU on TROSD, and +8.3% mIoU on Stanford2D3D, showing the effectiveness of our method against glass objects.
Abstract (translated)
玻璃是一种在日常生活中常见的固体材料，但由于其透明性和反射性，现有的分割方法难以将其与不透明材料区分开来。虽然已知人类感知依赖于边界特征和反射物体特征来区分玻璃物体，但现有文献尚未充分捕捉这些属性以处理透明物体。因此，我们提出通过边界特征增强模块（Boundary Feature Enhancement）和反射特征增强模块（Reflection Feature Enhancement）在相互促进的方式中引入这两种强大的视觉线索。 我们的方法TransCues采用了一种金字塔式变换器编码-解码架构来分割透明对象。实验证明了这两个模块可以有效结合使用，从而提高了各种基准数据集上的整体性能，包括玻璃物体语义分割、镜子物体语义分割以及通用的分割数据集。在Trans10K-v2上我们的方法比当前最佳水平高出4.2% mIoU，在MSD上高出5.6%，在RGBD-Mirror上高出10.1%，在TROSD上高出13.1%，在Stanford2D3D上高出8.3%，证明了我们对玻璃物体分割方法的有效性。
URL
https://arxiv.org/abs/2512.07034
