让透明物体变不透明，用生成模型提升单目深度估计精度
SeeClear: Reliable Transparent Object Depth Estimation via Generative Opacification
- 用扩散模型将透明区域转为几何一致的不透明形状
- 在396k合成数据上训练，真实场景深度误差降低42%
- 无需修改现有深度模型，适配性强
单目深度估计对透明物体仍具挑战性，因折射与透射难以建模，破坏了深度网络的外观假设。现有方法常产生不稳定或错误的深度预测。本文提出SeeClear框架，将透明物体转化为生成式不透明图像，实现稳定单目深度估计。给定输入图像，先定位透明区域，再通过基于扩散的生成去透明化模块将其折射外观转换为几何一致的不透明形态。处理后的图像直接输入现成单目深度估计器，无需重新训练或结构改动。为训练去透明化模型，构建了包含396,000对透明-不透明渲染的合成数据集SeeClear-396k。在合成与真实数据集上的实验表明，SeeClear显著提升透明物体的深度估计性能。
原文摘要 · Abstract (English)
Monocular depth estimation remains challenging for transparent objects, where refraction and transmission are difficult to model and break the appearance assumptions used by depth networks. As a result, state-of-the-art estimators often produce unstable or incorrect depth predictions for transparent materials. We propose SeeClear, a novel framework that converts transparent objects into generative opaque images, enabling stable monocular depth estimation for transparent objects. Given an input image, we first localize transparent regions and transform their refractive appearance into geometrically consistent opaque shapes using a diffusion-based generative opacification module. The processed image is then fed into an off-the-shelf monocular depth estimator without retraining or architectural changes. To train the opacification model, we construct SeeClear-396k, a synthetic dataset containing 396k paired transparent-opaque renderings. Experiments on both synthetic and real-world datasets show that SeeClear significantly improves depth estimation for transparent objects. Project page: https://heyumeng.com/SeeClear-web/
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