用视觉模型将透明物体转为有纹理的不透明体，提升3D重建精度。
GHOST: Geometry-Guided Hallucination of Opaque Surface Textures
- 通过分解透明属性与几何信息，生成结构一致的不透明图像。
- 在真实透明物体上使深度估计误差降低27.3%，3D重建质量显著提升。
- 无需重训练下游模型，适合科研与工业中复杂材质处理场景。
透明物体因违反朗伯假设，严重干扰深度估计与3D重建，导致下游任务几何失真。为此，我们提出新颖的几何引导预处理框架GHOST，利用视觉基础模型将透明区域转换为结构一致的不透明表示，且无需下游模型重训练。该流程首先通过TransDINO与TransDecomp分离掩码与透明物理属性，再由DAF-Net恢复表面法向先验以编码几何曲率；最后，GeoSemTransNet融合多模态线索，合成富含纹理的不透明RGB图像，完整保留透明物体的3D结构。大量实验表明，该方法显著提升当前主流深度估计与重建模型在透明物体上的准确性，有效恢复关键光照信息。
原文摘要 · Abstract (English)
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.
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