MG-SLAM：基于曼哈顿世界假设的结构高斯点撒SLAM
MG-SLAM: Structure Gaussian Splatting SLAM with Manhattan World Hypothesis
AI总结：
MG-SLAM基于曼哈顿世界假设，通过融合线段和平面假设提升室内场景重建的几何精度和完整性，实现高斯SLAM的先进性能。
摘要：
高斯点撒SLAM在提升实时重建效率和保真度方面取得了显著进展。然而，这些系统在复杂室内环境中常常面临不完整的重建问题，表现为由于障碍物或有限视角导致的大量孔洞。为解决这一挑战，我们提出了曼哈顿高斯SLAM，一种RGB-D系统，利用曼哈顿世界假设来提高几何精度和完整性。通过无缝整合从结构场景中提取的融合线段，我们的方法确保了在无纹理室内区域的鲁棒跟踪。此外，提取的线条和平面假设允许在缺失几何区域中进行策略性插值，从而高效地完成场景。在合成和真实场景上的大量实验表明，这些进步使我们的方法能够实现最先进的性能，标志着高斯SLAM系统能力的显著提升。
英文摘要：
Gaussian Splatting SLAMs have made significant advancements in improving the efficiency and fidelity of real-time reconstructions. However, these systems often encounter incomplete reconstructions in complex indoor environments, characterized by substantial holes due to unobserved geometry caused by obstacles or limited view angles. To address this challenge, we present Manhattan Gaussian SLAM, an RGB-D system that leverages the Manhattan World hypothesis to enhance geometric accuracy and completeness. By seamlessly integrating fused line segments derived from structured scenes, our method ensures robust tracking in textureless indoor areas. Moreover, The extracted lines and planar surface assumption allow strategic interpolation of new Gaussians in regions of missing geometry, enabling efficient scene completion. Extensive experiments conducted on both synthetic and real-world scenes demonstrate that these advancements enable our method to achieve state-of-the-art performance, marking a substantial improvement in the capabilities of Gaussian SLAM systems.
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