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  • SeeClear: Reliable Transparent Object Depth Estimation via Generative . . .
    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
  • SeeClear: Reliable Transparent Object Depth Estimation via Generative . . .
    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
  • SeeClear: Reliable Transparent Object Depth Estimation via Generative . . .
    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
  • SeeClear - heyumeng. com
    We propose SeeClear, a novel framework that converts transparent objects into generative opaque images, enabling stable monocular depth estimation for transparent objects
  • [分享] [每日更新] [2026. 03. 20] [ArXiv CV Paper] - 知乎
    Title: SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation Title_cn: SIMPLER: 通过相似性引导的层剪枝实现高效基础模型适配，用于Earth Observation
  • SeeClear: Reliable Transparent Object Depth Estimation via Generative . . .
    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
  • SeeClear: Reliable Transparent Object Depth Estimation via Generative . . .
    We propose SeeClear, a novel framework that converts transparent objects into generative opaque images, enabling stable monocular depth estimation for transparent objects
  • SeeClear: Reliable Transparent Object Depth Estimation via Generative . . .
    Abstract 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 estima-tors often produce unstable or incorrect depth predictions for transparent materials We propose SeeClear, a novel framework that converts transparent objects
  • Jingkai Shi - catalyzex. com
    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
  • Publication | UCLA Artificial Intelligence Visual Computing Lab
    SeeClear: Reliable Transparent Object Depth Estimation via Generative Opacification Xiaoying Wang*, Yumeng He*, Jingkai Shi* (equal contributions), Jiayin Lu, Yin Yang, Ying Jiang, Chenfanfu Jiang arXiv 2026





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