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English(EN) GenAssets: Generating in-the-wild 3D Assets in Latent Space

AI研究推动自动驾驶的3D资产生成和异常检测

研究人员开发了一种名为GenAssets的新方法,用于从野外的LiDAR和摄像头数据生成高质量的3D资产,这对于自动驾驶模拟至关重要。该方法采用“先重建后生成”的策略,首先构建详细的对象潜在空间,然后在该空间上训练扩散模型以生成完整的几何和外观。另外,另一项研究工作解决了在3D LiDAR数据中识别分布外对象以进行异常分割的挑战,这是自主系统的关键任务。这项工作引入了一种直接在特征空间中运行的新方法,并提出了混合真实-合成数据集以提高在复杂环境中的性能。 AI

影响 推动自动驾驶系统的3D资产生成和异常检测方面的进展,增强了模拟的真实感和安全性。

排序理由 arXiv上发布了两篇新研究论文,详细介绍了自动驾驶系统在3D资产生成和异常检测方面的进展。

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AI研究推动自动驾驶的3D资产生成和异常检测

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arXiv上发布了两篇新研究论文,详细介绍了自动驾驶系统在3D资产生成和异常检测方面的进展。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ze Yang, Jingkang Wang, Haowei Zhang, Sivabalan Manivasagam, Yun Chen, Raquel Urtasun ·

    GenAssets: 在潜在空间中生成野外3D资产

    arXiv:2604.23010v1 Announce Type: new Abstract: High-quality 3D assets for traffic participants are critical for multi-sensor simulation, which is essential for the safe end-to-end development of autonomy. Building assets from in-the-wild data is key for diversity and realism, bu…

  2. arXiv cs.CV TIER_1 English(EN) · Simone Mosco, Daniel Fusaro, Alberto Pretto ·

    学习识别用于3D LiDAR异常分割的分布外物体

    arXiv:2604.23604v1 Announce Type: new Abstract: Understanding the surrounding environment is fundamental in autonomous driving and robotic perception. Distinguishing between known classes and previously unseen objects is crucial in real-world environments, as done in Anomaly Segm…