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SemGauss-SLAM: Dense Semantic Gaussian Splatting SLAM
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SemGauss-SLAM: Dense Semantic Gaussian Splatting SLAM
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We propose SemGauss-SLAM, a dense semantic SLAM system utilizing 3D Gaussian representation, that enables accurate 3D semantic mapping, robust camera tracking, and high-quality rendering simultaneously. In this system, we incorporate semantic feature embedding into 3D Gaussian representation, which effectively encodes semantic information within the spatial layout of the environment for precise semantic scene representation. Furthermore, we propose feature-level loss for updating 3D Gaussian representation, enabling higher-level guidance for 3D Gaussian optimization. In addition, to reduce cumulative drift in tracking and improve semantic reconstruction accuracy, we introduce semantic-informed bundle adjustment. By leveraging multi-frame semantic associations, this strategy enables joint optimization of 3D Gaussian representation and camera poses, resulting in low-drift tracking and accurate semantic mapping. Our SemGauss-SLAM demonstrates superior performance over existing radiance field-based SLAM methods in terms of mapping and tracking accuracy on Replica and ScanNet datasets, while also showing excellent capabilities in high-precision semantic segmentation and dense semantic mapping.
Forward citations
Cited by 5 Pith papers
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RoGS: Adaptive Meshgrid Gaussian for Large-Scale Road Surface Mapping
RoGS reconstructs large-scale road surfaces with adaptive-grid 2D Gaussian surfels, reporting 53x faster training than mesh-based RoMe with comparable or better RGB, semantic, and elevation maps.
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DL-SLAM: Enabling High-Fidelity Gaussian Splatting SLAM in Dynamic Environments based on Dual-Level Probability
Dual-level semantic-geometric dynamic probabilities let monocular Gaussian SLAM use transiently static objects for tracking yet prune them from the map, cutting ATE up to 13% and removing artifacts.
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LEGO-SLAM: Language-Embedded Gaussian Optimization SLAM
A 3D Gaussian Splatting SLAM system learns compact 16-dim language features per Gaussian, enabling real-time open-vocabulary mapping, semantic pruning, and language-based loop closure.
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DL-SLAM: Enabling High-Fidelity Gaussian Splatting SLAM in Dynamic Environments based on Dual-Level Probability
DL-SLAM uses dual-level (pixel and object) dynamic probabilities from semantic-geometric fusion to produce artifact-free static maps and up to 13% better tracking accuracy in dynamic scenes.
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DSP-SLAM++: A Unified Framework for Multi-Class, High-Fidelity Object SLAM in the Wild
DSP-SLAM++ adds asynchronous mapping and fisheye-LiDAR fusion to DSP-SLAM, claiming up to 70% lower object processing latency and real-time performance on 25 Hz multi-class datasets while producing geometrically compl...
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