用神经结构光提升深度精度，实现高保真、稳定可靠的实时定位与建图。
NSL-SLAM: High-Fidelity Neural Structured-Light Depth for Practical SLAM and Reconstruction
- 融合单目深度先验，改进结构光解码，深度均方根误差降低35%。
- 以深度为主导设计SLAM系统，在真实场景中轨迹偏差减少43.3%。
- 适用于移动设备的高精度深度感知，适合需要稳定重建的实用场景。
结构光（SL）相机广泛用于数百万设备中的深度感知，近期神经结构光解码方法显著提升了深度质量。本工作提出NSL-SLAM，一种面向高保真结构光深度的实用化SLAM系统。首先，借鉴神经结构光（NSL）方法，进一步引入强单目深度先验至结构光立体解码中，在Replica-SL数据集上使深度均方根误差（RMSE）降低35%。随后构建以深度为核心的SLAM流水线：因结构光几何稠密且度量准确，将其作为主要跟踪信号，仅在几何退化情况下添加稀疏视觉对应与轻量级束调整以抑制长程漂移。深度估计与SLAM设计相互增强：更强的深度使简单管线有效，而深度主导的架构确保优势传递至下游重建。实验显示，在合成的Replica-SL基准上，NSL-SLAM达到最优跟踪精度，并在共享深度协议下使重建F-score提升1.6点；在8个真实挑战场景的基准上，它是唯一在所有序列中避免灾难性失败的方法，且轨迹偏差较选代基线降低43.3%。系统在线运行速度达20.9 FPS，证明更强结构光深度与深度主导设计共同实现高效鲁棒的实用化SLAM。
原文摘要 · Abstract (English)
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 long-range drift. Our depth estimator and SLAM design reinforce each other: stronger depth makes a simple SLAM pipeline effective, and the depth-centric pipeline ensures this advantage transfers to downstream reconstruction. Experimentally, on the synthetic Replica-SL benchmark, NSL-SLAM achieves the best tracking accuracy and improves reconstruction F-score by 1.6 points over the SOTA baseline under a shared-depth protocol. On a real benchmark of 8 challenging scenes, it is the only method that avoids catastrophic failure on all sequences while achieving 43.3% lower trajectory deviation than selected baselines. The SLAM system runs online at 20.9 FPS, demonstrating that stronger structured-light depth and depth-centric system design together enable practical, robust SLAM.
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