SLC$^2$-SLAM：面向 NeRF SLAM 的利用共享潜码的语义引导回环检测
SLC$^2$-SLAM: Semantic-guided Loop Closure using Shared Latent Code for NeRF SLAM
AI总结：
本文提出 SLC$^2$-SLAM，利用 NeRF 潜码及其解码语义检测并闭合回环，在 Replica 和 ScanNet 上显著改善位姿跟踪与场景重建。
摘要：
针对 NeRF SLAM 中众所周知的累积漂移误差，我们提出了一种利用共享潜码的语义引导回环闭合方法，称为 SLC$^2$-SLAM。我们认为，许多 NeRF SLAM 系统中存储的潜码并未得到充分利用，因为它们仅被用于改善重建效果。在本文中，我们提出了一种简单而有效的方法，将相同的潜码作为局部特征来检测潜在回环。为进一步提升回环检测性能，我们利用同样从这些潜码解码得到的语义信息来引导局部特征的聚合。最后，在检测到潜在回环后，我们通过图优化并随后进行光束法平差来闭合这些回环，以同时优化估计位姿和重建场景。为评估 SLC$^2$-SLAM 的性能，我们在 Replica 和 ScanNet 数据集上开展了大量实验。我们提出的语义引导回环闭合显著优于预训练 NetVLAD 以及 ORB 结合 Bag-of-Words 的方法，这些方法被用于所有其他具备回环闭合功能的 NeRF SLAM。因此，我们的 SLC$^2$-SLAM 也展现出更好的跟踪与重建性能，尤其是在 ScanNet 这类更大、包含更多回环的场景中。
英文摘要：
Targeting the notorious cumulative drift errors in NeRF SLAM, we propose a Semantic-guided Loop Closure using Shared Latent Code, dubbed SLC$^2$-SLAM. We argue that latent codes stored in many NeRF SLAM systems are not fully exploited, as they are only used for better reconstruction. In this paper, we propose a simple yet effective way to detect potential loops using the same latent codes as local features. To further improve the loop detection performance, we use the semantic information, which are also decoded from the same latent codes to guide the aggregation of local features. Finally, with the potential loops detected, we close them with a graph optimization followed by bundle adjustment to refine both the estimated poses and the reconstructed scene. To evaluate the performance of our SLC$^2$-SLAM, we conduct extensive experiments on Replica and ScanNet datasets. Our proposed semantic-guided loop closure significantly outperforms the pre-trained NetVLAD and ORB combined with Bag-of-Words, which are used in all the other NeRF SLAM with loop closure. As a result, our SLC$^2$-SLAM also demonstrated better tracking and reconstruction performance, especially in larger scenes with more loops, like ScanNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
