{"ID":6537693,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11118","arxiv_id":"2607.11118","title":"GHOST: Geometry-Guided Hallucination of Opaque Surface Textures","abstract":"Transparent objects pose a fundamental challenge for depth estimation and 3D reconstruction due to their violation of Lambertian assumptions, leading to severe geometry degradation in downstream tasks. To address this, we propose a novel geometry-guided preprocessing framework \\textbf{GHOST} that leverages visual foundation models to transform transparent regions into opaque, structurally consistent representations without requiring downstream model retraining. Specifically, our pipeline utilizes (1) \\textbf{TransDINO} and (2) \\textbf{TransDecomp} to disentangle masks and transparency physical properties, while (3) \\textbf{DAF-Net} recovers surface normal priors to encode geometric curvature. Subsequently, (4) \\textbf{GeoSemTransNet} integrates these multi-modal cues to synthesize a texture-rich opaque RGB image that preserves the transparent object's 3D structure. Extensive experiments demonstrate that our method significantly enhances the accuracy of state-of-the-art depth estimation and reconstruction models on transparent objects by restoring essential photometric cues.","short_abstract":"Transparent objects pose a fundamental challenge for depth estimation and 3D reconstruction due to their violation of Lambertian assumptions, leading to severe geometry degradation in downstream tasks. To address this, we propose a novel geometry-guided preprocessing framework \\textbf{GHOST} that leverages visual found...","url_abs":"https://arxiv.org/abs/2607.11118","url_pdf":"https://arxiv.org/pdf/2607.11118v1","authors":"[\"Langxu Zhao\",\"Zuan Gu\",\"Tianhan Gao\"]","published":"2026-07-13T05:50:19Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}