用神经高斯点云实现鲁棒实时三维重建
PointSLAM++: Robust Dense Neural Gaussian Point Cloud-based SLAM
- 分层约束神经高斯表示，保持结构一致性
- 逐级位姿优化降低深度噪声，定位更准
- 动态调整高斯节点分布，适应复杂场景
实时3D重建对机器人和增强现实至关重要，但现有SLAM方法在深度噪声下常难以维持结构一致性和鲁棒位姿估计。本文提出PointSLAM++，一种基于分层约束神经高斯表示的RGB-D SLAM系统，通过生成高斯原型进行场景建图，并采用逐级位姿优化缓解深度传感器噪声，显著提升定位精度。此外，系统利用动态神经表示图，根据局部几何复杂度自适应调整高斯节点分布，实现实时精细场景适应。该方法在重建精度与渲染质量上优于现有基于3DGS的SLAM方法，在大规模AR与机器人应用中表现突出。
原文摘要 · Abstract (English)
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.
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