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English(EN) LVSPM: Long Sequence View Synthesis and Pose Estimation Model

新模型应对新视图合成与相机姿态估计挑战

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新的研究论文介绍了从现有图像合成新视图和估计相机姿态的新颖方法。第一篇论文LVS，侧重于重用先前渲染的图像以改进交互式场景探索并减少渲染计算，特别适用于增强现实和虚拟现实应用。第二篇论文LVSPM，提出了一个可泛化的模型，该模型可以从未校准的图像集中联合估计相机姿态并合成新视图，在各种数据集上的姿态估计和新视图合成方面优于现有模型。 AI

影响 这些新颖的方法可以通过提高视觉保真度和降低计算负载来增强交互式场景探索以及增强现实和虚拟现实应用。

排序理由 两篇在arXiv上发表的学术论文，详细介绍了视图合成和姿态估计的新方法。

在 arXiv cs.CV 阅读 →

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新模型应对新视图合成与相机姿态估计挑战

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两篇在arXiv上发表的学术论文，详细介绍了视图合成和姿态估计的新方法。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Qizhou Huo, Xuan Sun, Yongfei Guo, Zhipeng Wang, Yuanhao Gong ·

    LVS：通过重用先前视图进行相对相机姿态的局部视图合成

    arXiv:2610.12127v1 Announce Type: cross Abstract: Interactive scene exploration requires frequent view updates, although small camera motions preserve much of the visible content. Conventional 3D Gaussian Splatting nevertheless renders each target view, leaving this image overlap…

  2. arXiv cs.CV TIER_1 English(EN) · Xi Chen, Yachi Zhang, Linghao Chen, Minghua Liu, Hao Su, Zexiang Xu, Xiaoshuai Zhang ·

    LVSPM：长序列视图合成与姿态估计模型

    arXiv:2610.10960v1 Announce Type: new Abstract: We present LVSPM, a generalizable model that jointly estimates camera poses and synthesizes novel views from uncalibrated image collections. Trained with only RGB images and pose supervision, LVSPM avoids dense 3D ground truth and e…

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