InstanceFlow-SLAM: Instance-Guided Continuous Motion Reconstruction for Dynamic Gaussian SLAM

Keyframe-based Gaussian SLAM leaves dynamic motion between map updates underconstrained, while increasing the mapping frequency raises computational cost. We introduce InstanceFlow-SLAM, which reconstructs continuous motion from intermediate RGB-D observations without additional Gaussian mapping up- dates. Instance identity restricts motion sharing among Gaussians, observations, and nodes, while adaptive node allocation captures local motion variation within each object. Continuous SE(3) B-splines represent node motion and are fitted to image tracks and depth-supported 3D observations. Reliable observations deter- mine each trajectory’s temporal support, preventing unsupported endpoints from distorting the fitted motion. After keyframe mapping, boundary-consistent mo- tion transfer preserves the recovered interior variation while aligning it with up- dated Gaussian states. Experiments on TUM RGB-D and Bonn demonstrate im- proved dynamic reconstruction with competitive camera localization. On Bonn, our method achieves the best average full-image reconstruction scores and the lowest average trajectory error among the compared methods, improving average dynamic-region PSNR by 1.44 dB over Flow4DGS-SLAM.