Article

TopoGS-SLAM: Reconstructing Gaussian SLAM densification strategy from a static sampling perspective

The authors propose TopoGS-SLAM, which decouples refinement from capacity using coverage-triggered Gaussian initialization and bounded-topology optimization, turning mapping into a bounded-dimensional problem. On standard indoor benchmarks, its localization and reconstruction accuracy is not inferior to densification-based pipelines, while memory scalability and throughput improve significantly. The authors therefore conclude that in bounded-area static indoor scenes with sufficient multi-view coverage, Gaussian SLAM does not require iterative topology mutation.

Part of the event: TopoGS-SLAM proposes stationary-sampling Gaussian SLAM method

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