详细信息
- 来源站点
- ArXiv CS.CV
- 作者
- Jiaheng Li, Binsheng Zhang, Xinhai Chang, Wenzheng Chen
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-07-28
摘要
arXiv:2607.24495v1 Announce Type: new Abstract: Structured-light (SL) cameras power depth sensing in millions of devices, and recent neural SL decoding methods have substantially improved their depth quality. SLAM systems can benefit greatly from such strong depth sensing, where reliable geometry enables stable tracking and faithful reconstruction. In this work, we present NSL-SLAM, a practical SLAM system tailored for high-fidelity structured-light depth. We first strengthen SL depth sensing: inspired by the neural structured-light (NSL) method, we further incorporate strong monocular depth priors into the SL stereo decoding, reducing depth RMSE by 35% on Replica-SL compared to NSL. We then build a depth-centric SLAM pipeline with this stronger depth: because structured-light geometry is dense and metrically accurate, we keep it as the primary tracking signal, and add only sparse visual correspondences for geometrically degenerate cases and lightweight bundle adjustment for…
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