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English(EN) RoboShape: Information-Theoretic Point Cloud Representations for Privacy-Aware Robot Perception

RoboShape 通过隐私保护的点云压缩增强机器人感知能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 RoboShape，一种用于机器人点云数据的新型信息论压缩方法。该技术旨在在保留物体分类效用的同时，显著降低与敏感空间信息相关的隐私风险。RoboShape 通过最大化嵌入与物体级别理解之间的互信息，同时最小化私有属性的互信息来实现这一点，从而得到更小、更高效的嵌入，便于传输和下游任务。 AI

影响 通过实现更高效、更安全地处理空间数据，增强了机器人感知系统中的隐私性。

排序理由 这是一篇详细介绍一种新的注重隐私的机器人感知方法的学术论文。

在 arXiv cs.AI 阅读 →

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RoboShape 通过隐私保护的点云压缩增强机器人感知能力

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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Oguzhan Baser, Mirac Sozen, Kaan Kale, Sandeep Chinchali, Sriram Vishwanath ·

    RoboShape：用于隐私感知机器人感知的信息论点云表示

    arXiv:2608.21380v1 Announce Type: cross Abstract: With the increased adoption of robotic agents operating in human environments by scanning and sharing 3D representations (e.g., for fleet learning, cloud-based planning, or collaborative mapping), collected point clouds reveal not…

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