FormFlow-GS: A General and Efficient Method for Online Multimodal Gaussian Mapping

Multimodal online Gaussian mapping still suffers from critical limitations in geometric formation and refinement efficiency. Although depth or LiDAR provides metric surface geometry, this information is often underused when new Gaussians are formed, while downstream refinement incurs substantial execution overhead. Addressing both stages is therefore essential for high-quality and efficient online mapping. We propose FormFlow-GS, a general Gaussian mapping method built around two components. GeoBirth uses local metric geometry and neighborhood reliability to form surface-aligned Gaussians with adaptive anisotropic support, providing a better-conditioned geometric starting point for refinement. Coalesced Gaussian Optimization (CGO) coalesces heterogeneous attribute updates over a shared visible-Gaussian workset, reducing redundant execution during refinement. The design is broadly applicable and can be readily integrated into Gaussian mapping backbones spanning monocular, RGB-D, RGB-LiDAR, and LiDAR-RGB-IMU settings. Across these backbones and multimodal benchmarks, it consistently improves rendering quality and online mapping efficiency, achieving a strong quality-efficiency balance and outperforming most evaluated state-of-the-art baselines overall.