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English(EN) Digital Twin-Driven Real2Sim2Real: Simulator-Conditioned Generation via Paired Driving-Scene Reconstruction

数字孪生管线为自动驾驶汽车生成合成驾驶数据

研究人员开发了一种名为数字孪生驱动的Real2Sim2Real (DT-R2S2R) 的新颖管线,用于为自动驾驶汽车感知系统生成合成驾驶数据。该方法在数字孪生中重建真实世界的驾驶片段,使扩散模型能够合成经过几何对齐的模拟器渲染的条件下的照片级真实图像。事实证明,生成的数据可以显著减少在目标区域进行昂贵的手动数据收集和标注的需求,其中一个检测器在没有直接在目标图像上进行训练的情况下,达到了真实数据神谕性能的93%以上。 AI

影响 通过降低数据收集成本,这种方法可以显著降低开发和部署自动驾驶汽车感知系统的门槛。

排序理由 该集群包含一篇详细介绍合成数据生成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

数字孪生管线为自动驾驶汽车生成合成驾驶数据

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该集群包含一篇详细介绍合成数据生成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Hojun Lim, Hyeongseok Jeon, Donghyun Kim, Soonyoung Jung, Heecheol Yoo ·

    数字孪生驱动的 Real2Sim2Real:通过配对驾驶场景重建实现模拟器条件生成

    arXiv:2610.08339v1 Announce Type: new Abstract: Camera-based 3D perception for autonomous driving relies heavily on large annotated datasets, and deploying such a system to a new target region typically requires data collection and annotation. Generative augmentation has been pro…