Abstract
With the emergence of Neural Radiance Fields (NeRF), neural implicit representations have gained widespread applications across various domains, including simultaneous localization and mapping. However, current neural implicit SLAM faces a challenging trade-off problem between performance and the number of parameters. To address this problem, we propose sparse tri-plane encoding, which efficiently achieves scene reconstruction at resolutions up to 512 using only 2~4% of the commonly used tri-plane parameters (reduced from 100MB to 2~4MB). On this basis, we design S3-SLAM to achieve rapid and high-quality tracking and mapping through sparsifying plane parameters and integrating orthogonal features of tri-plane. Furthermore, we develop hierarchical bundle adjustment to achieve globally consistent geometric structures and reconstruct high-resolution appearance. Experimental results demonstrate that our approach achieves competitive tracking and scene reconstruction with minimal parameters on three datasets. Source code will soon be available.
Abstract (translated)
随着Neural Radiance Fields（NeRF）的出现，神经隐式表示已经在各种领域得到了广泛应用，包括同时定位和映射。然而，当前的神经隐式SLAM在性能和参数数量之间存在一个具有挑战性的权衡问题。为了解决这个问题，我们提出了稀疏三平面编码，它通过仅使用2~4%的常用三平面参数（从100MB减少到2~4MB）在高达512的分辨率下高效地实现场景重构。在此基础上，我们设计了一个S3-SLAM，通过稀疏化三平面的参数并整合三平面的正交特征，实现了快速且高质量的跟踪和映射。此外，我们还开发了层次结构Bundle Adjustment以实现全局一致的几何结构和重构高分辨率的外观。实验结果表明，我们的方法在三个数据集上实现了具有竞争力的跟踪和场景重构，且参数数量最小。源代码不久将可用。
URL
https://arxiv.org/abs/2404.18284
