$\Omega$-SLAM: Omega-Native Loop Closure for Uncalibrated Dynamic SLAM

We propose Ω-SLAM, a training-free dense RGB SLAM system built on VGGT-Ω for uncalibrated monocular videos. Extending feed-forward reconstruction to long sequences requires loop constraints that remain reliable under perceptual aliasing and scene motion. We address this challenge by using VGGT-Ω’s own representations for both retrieval and geometric verification, eliminating a separate place-recognition network. Pre-attention patch embeddings retrieve candidate frames, while compact scene registers provide submap context for reranking. Jointly predicted cameras and depths then support a depth-conditioned rigid reprojection test on mutual patch correspondences, checking geometric consistency beyond epipolar agreement. Verified loops are integrated as quality-weighted robust factors in an SL(4) graph, with multi-anchor calibration bridges stabilizing submap alignment and geometry-based observation weights reducing the influence of moving content. Retrieval state uses 96 KiB for 16 frames, while a fixed set of active submaps bounds resident dense-map memory. Experiments reduce mean absolute trajectory error relative to VGGT-SLAM 2.0 by 36.5% on 7-Scenes and 47.2% across Bonn and TUM dynamic sequences, demonstrating improved trajectory accuracy without additional training or an external retrieval network.