[
    {
        "id": "osp-23932",
        "type": "article-journal",
        "title": "Depth Hypothesis Guided Iterative Refinement for Event-Image Monocular Depth Estimation",
        "author": [
            {
                "family": "Liu",
                "given": "Daikun"
            },
            {
                "family": "Wang",
                "given": "Teng"
            },
            {
                "family": "Sun",
                "given": "Changyin"
            }
        ],
        "URL": "https://omanscience.com/en/articles/depth-hypothesis-guided-iterative-refinement-for-event-image-monocular-depth-estimation",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Event cameras hold excellent dynamic properties, showing great potential for monocular depth estimation (MDE). However, existing methods mainly improve performance by optimizing contextual features, but still struggle with the ill-posed and nonlinear nature of direct full-depth regression. In this paper, we propose HypoDepth, the first event-image monocular depth iterative refinement framework. By introducing a discrete Depth Hypothesis Volume (DHV), we transform the depth regression problem into a constrained depth search task. Specifically, we construct a 3D cost volume between the DHV features and contextual features and perform a multi-scale correlation search to guide stable residual optimization. This lightweight cost volume enables efficient global-to-local refinement across multi-resolution. Our method outperforms existing approaches on DSEC and MVSEC with state-of-the-art results and strong zero-shot generalization. Meanwhile, our tiny model achieves an excellent balance between accuracy and efficiency, enabling real-time performance on resource-limited devices."
    }
]