M^3：密集匹配与多视角基础模型结合的单目高斯点云SLAM
M^3: Dense Matching Meets Multi-View Foundation Models for Monocular Gaussian Splatting SLAM
AI总结：
本文提出M^3，结合多视角基础模型与单目高斯点云SLAM，通过引入匹配头提升密集对应关系，增强跟踪稳定性，在多种基准上取得优异的位姿估计和场景重建性能。
摘要：
流式重建仍面临挑战，需高精度姿态估计和动态环境中的高效在线优化。尽管将3D基础模型与SLAM框架结合是可行的，但多数多视角基础模型以 feed-forward 方式估计姿态，导致像素级对应关系精度不足。为此，本文提出M^3，通过引入专用匹配头促进细粒度密集对应关系，并将其集成到稳健的单目高斯点云SLAM中。M^3进一步通过动态区域抑制和跨推理内在对齐提升跟踪稳定性。在多种室内和室外基准上的实验表明，在位姿估计和场景重建方面均达到最先进的性能。值得注意的是，M^3相比VGGT-SLAM 2.0将ATE RMSE降低了64.3%，在ScanNet++数据集上PSNR优于ARTDECO 2.11 dB。
英文摘要：
Streaming reconstruction from uncalibrated monocular video remains challenging, as it requires both high-precision pose estimation and computationally efficient online refinement in dynamic environments. While coupling 3D foundation models with SLAM frameworks is a promising paradigm, a critical bottleneck persists: most multi-view foundation models estimate poses in a feed-forward manner, yielding pixel-level correspondences that lack the requisite precision for rigorous geometric optimization. To address this, we present M^3, which augments the Multi-view foundation model with a dedicated Matching head to facilitate fine-grained dense correspondences and integrates it into a robust Monocular Gaussian Splatting SLAM. M^3 further enhances tracking stability by incorporating dynamic area suppression and cross-inference intrinsic alignment. Extensive experiments on diverse indoor and outdoor benchmarks demonstrate state-of-the-art accuracy in both pose estimation and scene reconstruction. Notably, M^3 reduces ATE RMSE by 64.3% compared to VGGT-SLAM 2.0 and outperforms ARTDECO by 2.11 dB in PSNR on the ScanNet++ dataset.
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