TL;DR
DynGS-SLAM is proposed, a robust, dynamic-aware 3D Gaussian Splatting framework based on SGS-SLAM that outperforms SGS-SLAM across all metrics on dynamic sequences of the TUM dataset and other datasets.
This work proposes Robust Semantic-aware Gaussian Splatting SLAM (RoSe-SLAM), to address the dynamic challenge by a holistic semantic scene understanding from uncalibrated monocular inputs, achieving accurate camera tracking and high-quality geometry reconstruction.
3D Gaussian Splatting has enabled photorealistic dense mapping for visual Simultaneous Localization and Mapping (SLAM). Nevertheless, most existing pipelines are designed for static scenes and thus degrade under dynamic occlusions, leading to unstable tracking and noticeable mapping artifacts. Prior dynamic solutions o...
This work revisits 3D Gaussian Splatting heuristics in a decoupled 3DGS-SLAM setting and proposes three geometry-aware methods that operate in the mapping thread: transmittance-preserving densification, camera-aware scale initialization from depth and intrinsics, and error-guided densification that focuses new primitiv...
The proposed Semantic and Geometric Adaptive SLAM system effectively suppresses dynamic artifacts and point-cloud contamination in dense mapping, generating static environment maps with clearer structures and improved geometric consistency.
Recent advances in SLAM systems based on 3D Gaussian Splatting have enabled dense and photorealistic scene reconstruction. However, 3DGS‐based methods still suffer from limited geometric awareness due to the lack of normal rendering support, view inconsistency and weak depth constraints, ultimately hindering high‐f...
This work presents CGS-SLAM, a hybrid decentralized/centralized system enabling multi-agent 3DGS SLAM using only RGB and inertial data, and demonstrates competitive tracking performance, improved rendering quality over state-of-the-art methods, and accurate submap alignment.
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