MonoGlass3D: 平面回帰と適応的特徴融合による単眼3Dガラス検出
MonoGlass3D: Monocular 3D Glass Detection with Plane Regression and Adaptive Feature Fusion
実世界のガラスを3Dアノテーション付きで集めた新データセットを構築し、適応的特徴融合と平面回帰を組み合わせて単眼画像からガラスの領域と奥行きを推定する手法を提案した。
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著者: Kai Zhang, Guoyang Zhao, Jianxing Shi, Bonan Liu, Weiqing Qi, Jun Ma
分類: cs.RO
原文アブストラクト
Detecting and localizing glass in 3D environments poses significant challenges for visual perception systems, as the optical properties of glass often hinder conventional sensors from accurately distinguishing glass surfaces. The lack of real-world datasets focused on glass objects further impedes progress in this field. To address this issue, we introduce a new dataset featuring a wide range of glass configurations with precise 3D annotations, collected from distinct real-world scenarios. On the basis of this dataset, we propose MonoGlass3D, a novel approach tailored for monocular 3D glass detection across diverse environments. To overcome the challenges posed by the ambiguous appearance and context diversity of glass, we propose an adaptive feature fusion module that empowers the network to effectively capture contextual information in varying conditions. Additionally, to exploit the distinct planar geometry of glass surfaces, we present a plane regression pipeline, which enables seamless integration of geometric properties within our framework. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches in both glass segmentation and monocular glass depth estimation. Our results highlight the advantages of combining geometric and contextual cues for transparent surface understanding.
