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GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting
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GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting
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In this paper, we introduce \textbf{GS-SLAM} that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping (SLAM) system. It facilitates a better balance between efficiency and accuracy. Compared to recent SLAM methods employing neural implicit representations, our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Specifically, we propose an adaptive expansion strategy that adds new or deletes noisy 3D Gaussians in order to efficiently reconstruct new observed scene geometry and improve the mapping of previously observed areas. This strategy is essential to extend 3D Gaussian representation to reconstruct the whole scene rather than synthesize a static object in existing methods. Moreover, in the pose tracking process, an effective coarse-to-fine technique is designed to select reliable 3D Gaussian representations to optimize camera pose, resulting in runtime reduction and robust estimation. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets. Project page: https://gs-slam.github.io/.
Forward citations
Cited by 7 Pith papers
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Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation
Hallo4D uses vision-language models to detect and correct spatial and temporal mistakes in AI-generated 3D and 4D content, improving consistency without retraining the base generators.
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Compact 3D Gaussian Splatting For Dense Visual SLAM
A compact 3D Gaussian Splatting SLAM system reduces Gaussian count and parameter size via masking and a geometry codebook while preserving SOTA reconstruction quality and pose accuracy.
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GLidE-SLAM: GL-Accelerated Indirect-Direct Embedded SLAM
GLidE-SLAM moves pose-only photometric tracking to OpenGL ES compute shaders, reporting up to 9x faster frame rates than ORB-SLAM2 on embedded platforms with comparable ATE on TUM and EuRoC sequences.
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Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation
Hallo4D mitigates 3D/4D generation hallucinations via LMM-based detection, multi-model voting correction, and motion-aware optimization without retraining base generators.
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Pocket-SLAM: Rendering-Area-Aware Pruning for Memory-Efficient 3DGS-SLAM
Pocket-SLAM introduces rendering-area-aware pruning for 3DGS-SLAM, claiming over 60% memory reduction and 2x FPS gain on EuRoC and KITTI while keeping localization and mapping accuracy.
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Flow4DGS-SLAM: Optical Flow-Guided 4D Gaussian Splatting SLAM
Flow4DGS-SLAM uses optical flow to generate motion masks, initialize poses, and guide 4D Gaussian modeling with scene flow and GMM for temporal properties, claiming SOTA results in dynamic tracking and reconstruction.
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DiskChunGS: Large-Scale 3D Gaussian SLAM Through Chunk-Based Memory Management
Storing inactive spatial chunks of a 3D Gaussian map on disk and loading only camera-visible chunks into GPU memory lets DiskChunGS map all 11 KITTI sequences on a 24 GB GPU without memory failures.
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