English(EN) Depth Hypothesis Guided Iterative Refinement for Event-Image Monocular Depth Estimation

HypoDepth框架精炼事件图像深度估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员推出HypoDepth，一个利用事件图像数据的单目深度估计新框架。该方法通过采用离散的深度假设体积（DHV）将深度回归问题转化为约束搜索任务。通过构建3D成本体积并执行多尺度相关搜索，HypoDepth引导稳定的残差优化，在DSEC和MVSEC基准测试中表现优于现有方法，取得了最先进的结果和强大的零样本泛化能力。该框架的轻量级设计还使其能够在资源受限的设备上实现实时性能。 AI

影响这项研究推进了单目深度估计技术，有可能在计算能力有限的设备上实现更高效的实时应用。

排序理由该集群包含一篇详细介绍新方法和基准测试结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Daikun Liu, Teng Wang, Changyin Sun · 2026-10-05 04:00

基于深度假设的迭代精炼用于事件图像单目深度估计

arXiv:2610.03439v1 Announce Type: new 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-p…

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基于深度假设的迭代精炼用于事件图像单目深度估计

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