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English(EN) Monocular Depth Estimation via Neural Network with Learnable Algebraic Group and Ring Structures

通过具有可学习代数群和环结构的神经网络进行单目深度估计

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了LAGRNet，这是一个用于单目深度估计的新框架，它融入了代数几何原理。与先前将深度估计视为通用回归问题的先前方法不同，LAGRNet明确地将可学习的群、环和层结构嵌入其深度学习流程中。这种方法旨在强制执行投影等变性并确保全局拓扑一致性，从而在KITTI和NYU-Depth V2等基准测试中提高准确性和泛化能力。 AI

影响 引入了一种新颖的代数几何方法来进行深度估计，有望提高计算机视觉任务的准确性和泛化能力。

排序理由 介绍用于单目深度估计的新型神经网络架构的学术论文。

在 arXiv cs.CV 阅读 →

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

通过具有可学习代数群和环结构的神经网络进行单目深度估计

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介绍用于单目深度估计的新型神经网络架构的学术论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Qianlei Wang, Kexun Chen, Shaolin Zhang, Hongli Gao, Chaoning Zhang, Xiaolin Qin ·

    通过具有可学习代数群和环结构的神经网络进行单目深度估计

    arXiv:2604.24328v1 Announce Type: new Abstract: Monocular depth estimation (MDE) has witnessed remarkable progress driven by Convolutional Neural Networks and transformer-based architectures. However, these approaches typically treat the problem as a generic image-to-image regres…

  2. arXiv cs.CV TIER_1 English(EN) · Xiaolin Qin ·

    基于具有可学习代数群和环结构的神经网络的单目深度估计

    Monocular depth estimation (MDE) has witnessed remarkable progress driven by Convolutional Neural Networks and transformer-based architectures. However, these approaches typically treat the problem as a generic image-to-image regression on Euclidean grids, thereby overlooking the…

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