融合结构监督与混合表征，提升稠密SLAM的重建精度与全局一致性。
HS-SLAM: Hybrid Representation with Structural Supervision for Improved Dense SLAM
- 采用哈希网格、三平面与单点混合编码，增强场景表征能力
- 通过非局部像素块采样实现结构监督，更好捕捉场景结构
- 引入主动全局捆绑调整，有效抑制相机漂移与累积误差
基于NeRF的SLAM近期在跟踪与重建方面取得显著进展。然而，现有方法在场景表征能力、结构信息捕捉以及大运动或遗忘场景下的全局一致性方面仍面临挑战。为此，本文提出HS-SLAM。为提升场景表征容量，设计一种混合编码网络，融合哈希网格、三平面与单点（one-blob）的优势，提升重建的完整性和平滑性。同时，通过采样非局部像素块而非单条光线，引入结构监督，更有效地捕获场景结构。为保障全局一致性，实施主动全局捆绑调整（active global BA），消除相机漂移并缓解累积误差。实验结果表明，HS-SLAM在跟踪与重建精度上优于基线方法，同时保持机器人应用所需的效率。
原文摘要 · Abstract (English)
NeRF-based SLAM has recently achieved promising results in tracking and reconstruction. However, existing methods face challenges in providing sufficient scene representation, capturing structural information, and maintaining global consistency in scenes emerging significant movement or being forgotten. To this end, we present HS-SLAM to tackle these problems. To enhance scene representation capacity, we propose a hybrid encoding network that combines the complementary strengths of hash-grid, tri-planes, and one-blob, improving the completeness and smoothness of reconstruction. Additionally, we introduce structural supervision by sampling patches of non-local pixels rather than individual rays to better capture the scene structure. To ensure global consistency, we implement an active global bundle adjustment (BA) to eliminate camera drifts and mitigate accumulative errors. Experimental results demonstrate that HS-SLAM outperforms the baselines in tracking and reconstruction accuracy while maintaining the efficiency required for robotics.
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