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English(EN) CARD: A Multi-Modal Automotive Dataset for Dense 3D Reconstruction in Challenging Road Topography

新的CARD数据集为崎岖地形上的自动驾驶提供密集三维重建

研究人员推出CARD,这是一个新推出的多模态汽车数据集,专为在具有挑战性的道路条件下进行密集三维重建而设计。与专注于铺装良好的道路的现有数据集不同,CARD为坑洼和越野路段等不规则表面提供了准密集三维地面实况。该数据集包括来自立体摄像头、LiDAR和运动传感器的同步数据,覆盖德国和意大利约110公里。 AI

影响 为改进在复杂地形上运行的自动驾驶系统中的深度估计和感知提供了一个新的基准数据集。

排序理由 这是一篇介绍用于计算机视觉任务的新数据集的研究论文。

在 arXiv cs.CV 阅读 →

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新的CARD数据集为崎岖地形上的自动驾驶提供密集三维重建

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这是一篇介绍用于计算机视觉任务的新数据集的研究论文。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Gasser Elazab, Frank Neuhaus, Tilman Ko{\ss}, Malte Splietker, Aditya Date, Michael Unterreiner, Maximilian Jansen, Olaf Hellwich ·

    CARD:一个用于复杂道路地形密集三维重建的多模态汽车数据集

    arXiv:2605.05014v1 Announce Type: new Abstract: Autonomous driving must operate across diverse surfaces to enable safe mobility. However, most driving datasets are captured on well-paved flat roads. Moreover, recent driving datasets primarily provide sparse LiDAR ground truth for…

  2. arXiv cs.CV TIER_1 English(EN) · Olaf Hellwich ·

    CARD:一个用于复杂道路地形密集三维重建的多模态汽车数据集

    Autonomous driving must operate across diverse surfaces to enable safe mobility. However, most driving datasets are captured on well-paved flat roads. Moreover, recent driving datasets primarily provide sparse LiDAR ground truth for images, which is insufficient for assessing fin…