Real-time 3D reconstruction is crucial for robotics and augmented reality, yet current simultaneous localization and mapping(SLAM) approaches often struggle to maintain structural consistency and robust pose estimation in the presence of depth noise. This work introduces PointSLAM++, a novel RGB-D SLAM system that leverages a hierarchically constrained neural Gaussian representation to preserve structural relationships while generating Gaussian primitives for scene mapping. It also employs progressive pose optimization to mitigate depth sensor noise, significantly enhancing localization accuracy. Furthermore, it utilizes a dynamic neural representation graph that adjusts the distribution of Gaussian nodes based on local geometric complexity, enabling the map to adapt to intricate scene details in real time. This combination yields high-precision 3D mapping and photorealistic scene rendering. Experimental results show PointSLAM++ outperforms existing 3DGS-based SLAM methods in reconstruction accuracy and rendering quality, demonstrating its advantages for large-scale AR and robotics.

翻译：实时三维重建对于机器人和增强现实至关重要，然而现有的同步定位与建图（SLAM）方法在深度噪声存在时，往往难以维持结构一致性并实现鲁棒的位姿估计。本文提出了PointSLAM++，一种新颖的RGB-D SLAM系统，它利用分层约束的神经高斯表示来保持结构关系，同时生成用于场景建图的高斯图元。该系统还采用渐进式位姿优化来减轻深度传感器噪声，显著提升了定位精度。此外，它利用动态神经表示图，根据局部几何复杂度调整高斯节点的分布，使地图能够实时适应复杂的场景细节。这种组合实现了高精度的三维建图与逼真的场景渲染。实验结果表明，PointSLAM++在重建精度和渲染质量上均优于现有的基于3DGS的SLAM方法，证明了其在大规模增强现实与机器人应用中的优势。