English(EN) Preserving Guidance in Cost-Volume Retrieval under Extreme LiDAR Sparsity in Iterative Stereo

新的GRAFT-Stereo方法增强了激光雷达引导的立体匹配

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种名为Guided RAFT-Stereo (GRAFT-Stereo) 的新方法，以利用稀疏的激光雷达数据改进立体匹配。他们的分析表明，现有的迭代立体方法难以有效利用极其稀疏的激光雷达输入，导致引导精度下降。为解决此问题，他们提出预填充初始视差图，从而提高成本体积检索的可靠性。尽管机制不同，但这种预填充技术在通过早期融合将激光雷达深度整合到图像特征中时也很有益。通过结合这些方法，GRAFT-Stereo在各种数据集上均显示出比以前的激光雷达引导立体方法有显著改进。 AI

影响这项研究通过利用稀疏传感器数据改进立体匹配，有望在自主系统中实现更准确、更具成本效益的3D重建。

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arXiv cs.CV TIER_1 English(EN) · Jinsu Yoo, Sooyoung Jeon, Zanming Huang, Tai-Yu Pan, Wei-Lun Chao · 2026-09-15 04:00

在迭代立体声的极端激光雷达稀疏性下保留成本-体积检索中的引导

arXiv:2507.19738v2 Announce Type: replace Abstract: While accurate LiDAR depth has been shown to improve stereo matching, high-end LiDAR remains costly and difficult to deploy at scale, motivating guidance from sparse LiDAR measurements. In this paper, we revisit how extremely sp…

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在迭代立体声的极端激光雷达稀疏性下保留成本-体积检索中的引导

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