NEDS-SLAM：一种使用3D高斯泼溅的神经显式稠密语义SLAM框架
NEDS-SLAM: A Neural Explicit Dense Semantic SLAM Framework using 3D Gaussian Splatting
AI总结：
本文提出NEDS-SLAM，一种基于3D高斯表示的稠密语义SLAM系统，通过空间一致特征融合、紧凑语义特征编码和虚拟视角剪枝，在Replica和ScanNet数据集上实现了具有竞争力的建图与跟踪精度及高质量语义建图。
摘要：
我们提出NEDS-SLAM，一种基于3D高斯表示的稠密语义SLAM系统，能够实时实现鲁棒的3D语义建图、精确的相机跟踪和高质量渲染。在该系统中，我们提出了一种空间一致特征融合模型，以减少预训练分割头对语义重建的错误估计影响，从而实现鲁棒的3D语义高斯建图。此外，我们采用轻量级编码器-解码器将高维语义特征压缩为紧凑的3D高斯表示，缓解了过多内存消耗的负担。进一步地，我们利用3D高斯泼溅的优势，实现高效且可微的新视角渲染，并提出了一种虚拟相机视角剪枝方法以消除离群高斯，从而有效提升场景表示的质量。我们的NEDS-SLAM方法在Replica和ScanNet数据集上，在建图和跟踪精度方面相较于现有稠密语义SLAM方法表现出具有竞争力的性能，同时在3D稠密语义建图方面也展现出卓越的能力。
英文摘要：
We propose NEDS-SLAM, a dense semantic SLAM system based on 3D Gaussian representation, that enables robust 3D semantic mapping, accurate camera tracking, and high-quality rendering in real-time. In the system, we propose a Spatially Consistent Feature Fusion model to reduce the effect of erroneous estimates from pre-trained segmentation head on semantic reconstruction, achieving robust 3D semantic Gaussian mapping. Additionally, we employ a lightweight encoder-decoder to compress the high-dimensional semantic features into a compact 3D Gaussian representation, mitigating the burden of excessive memory consumption. Furthermore, we leverage the advantage of 3D Gaussian splatting, which enables efficient and differentiable novel view rendering, and propose a Virtual Camera View Pruning method to eliminate outlier gaussians, thereby effectively enhancing the quality of scene representations. Our NEDS-SLAM method demonstrates competitive performance over existing dense semantic SLAM methods in terms of mapping and tracking accuracy on Replica and ScanNet datasets, while also showing excellent capabilities in 3D dense semantic mapping.
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。
