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English(EN) Extending Foundational Monocular Depth Estimators to Fisheye Cameras with Calibration Tokens

新方法改进鱼眼相机深度估计 · 跟踪到2个来源

研究人员开发了新的方法来改进鱼眼相机的深度估计。一种方法,“校准令牌”,通过对齐潜在嵌入来适应现有的基础单目深度估计器以用于鱼眼图像,而无需重新训练。另一种方法 OmniDS 使用双流上下文融合技术和迭代细化来处理全向相机设备固有的可见性冲突。这两种方法都旨在提高鱼眼相机系统深度估计的准确性和适用性。 AI

影响 增强了鱼眼相机在需要精确 3D 场景理解的应用中的实用性。

排序理由 两篇不同的研究论文提出了用于鱼眼相机深度估计的新颖方法。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新方法改进鱼眼相机深度估计 · 跟踪到2个来源

报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Rit Gangopadhyay, Jung-Hee Kim, Xien Chen, Patrick Rim, Hyoungseob Park, Alex Wong ·

    Extending Foundational Monocular Depth Estimators to Fisheye Cameras with Calibration Tokens

    arXiv:2508.04928v5 Announce Type: replace-cross Abstract: We propose a method to extend foundational monocular depth estimators (FMDEs), trained on perspective images, to fisheye images. Despite being trained on tens of millions of images, FMDEs are susceptible to the covariate s…

  2. arXiv cs.CV TIER_1 English(EN) · Chaesong Park, Jihyeon Hwang, Muyeol Sung, Jongwoo Lim ·

    OmniDS: Dual-Stream Context Fusion for Omnidirectional Depth from Fisheye Cameras

    arXiv:2607.03038v1 Announce Type: new Abstract: Omnidirectional depth estimation from multi-fisheye camera rigs is complicated by visibility conflicts: wide baselines cause different cameras to observe different portions, or even different faces, of the same object, so aggregatin…