Zhisong Xu

Fan Zhu, Ziyu Chen, Zhenjun Zhao et al.

Real-time monocular Simultaneous Localization and Mapping (SLAM) fundamentally suffers from scale ambiguity and a lack of geometric self-correction. While 3D Gaussian Splatting (3DGS) enables high-fidelity rendering, existing RGB-only systems remain open-loop because depth priors are injected into mapping but refined geometry cannot effectively regulate tracking drift. We present MyGO-Splat, a closed-loop Gaussian SLAM framework that analytically rasterizes Gaussian primitives into pixel-wise depth and surface normals, allowing the map to actively supervise camera pose optimization. To bridge monocular priors and scale consistency, our framework introduces scale-aware adaptive alignment that projects foundation-model depth estimates into the globally optimized Gaussian space, forming a self-correcting cycle for scale feedback. Extensive evaluations show that this closed-loop design improves scale stability and appearance-geometry consistency, achieving performance comparable to RGB-D methods while using only monocular input.

Zhisong Xu, Takeshi Oishi

Panoramic sensing offers wide field-of-view coverage, yet 3D reconstruction from sparse panoramas remains challenging under rotation-dominant, weak-parallax motion. In such regimes, SfM/SLAM initialization is often ill-conditioned and unreliable. We present PanoImager, an SfM-free framework that combines feed-forward pose/depth priors, geometry-conditioned diffusion view completion, and depth-guided 3DGS optimization. Given only a few panoramic images, PanoImager decomposes them into local perspective views, synthesizes auxiliary observations to enrich sparse evidence, and stabilizes Gaussian optimization for improved cross-view consistency. Experiments on multiple benchmarks show improved stability under extreme sparsity, suggesting PanoImager as an offline/background component for map refinement when SfM/SLAM fails to initialize.

Fan Zhu, Ziyu Chen, Peichen Liu et al.

3D Gaussian Splatting (3DGS) has significantly boosted novel view synthesis and high-fidelity scene reconstruction, expanding the potential of 3DGS-based Visual Simultaneous Localization and Mapping (SLAM) methods. However, most existing systems fail to fully exploit the underlying structural information, which limits rendering quality and often leads to inconsistent maps. To address these limitations, we propose MMD-SLAM, a structure-enhanced Visual SLAM framework that leverages the Atlanta World (AW) assumption to guide a Multi-Meta Gaussian representation for photorealistic mapping. First, we introduce a point-line fusion strategy for pose optimization, where 3D line segments are incorporated to improve tracking robustness and provide additional constraints for mapping. Second, we design a Multi-Meta Gaussian representation with dominant directions, explicitly encoding structural priors from the AW hypothesis. Finally, we propose a Gaussian evolution strategy that adapts to scene geometry and incorporates structural cues into global optimization. Extensive experiments demonstrate that these innovations enable MMD-SLAM to achieve state-of-the-art performance in both tracking accuracy and mapping quality. e.g., our method achieves a 48.56% reduction in ATE RMSE on ScanNet and a 5.71% improvement in PSNR on Replica, compared with MonoGS.

Zhisong Xu, Takeshi Oishi

Streaming Visual Geometry Transformers such as StreamVGGT enable strong online 3D perception but suffer from unbounded KV-cache growth, which limits deployment over long streams. We revisit bounded-memory streaming from the perspective of geometric support. In geometry-driven reasoning, memory quality depends not only on how many tokens are retained, but also on whether the retained memory still preserves sufficiently coherent local support. This suggests that token-level retention may become less suitable under fixed budgets, as it can thin the evidence available within each contributing frame and make subsequent fusion more sensitive to weakly aligned history. Motivated by this observation, we propose FrameVGGT, a frame-driven rolling explicit-memory framework that treats each frame's incremental KV contribution as a coherent evidence block. FrameVGGT summarizes each block into a compact prototype and maintains a fixed-capacity mid-term bank of complementary frame blocks under strict budgets, with an optional lightweight anchor tier for rare prolonged degradation. Across long-sequence 3D reconstruction, video depth estimation, and camera pose benchmarks, FrameVGGT achieves favorable accuracy--memory trade-offs under bounded memory, while maintaining more stable geometry over long streams.