KiloGS-SLAM is a monocular 3DGS SLAM system with condition-triggered hybrid tracking and probabilistic chunk-based Gaussian mapping that scales to over 10,000 frames in outdoor environments while maintaining accuracy and efficiency.
Available: https://arxiv.org/abs/2103.00933
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
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VGGT-Long extends VGGT with chunking, overlap alignment, and loop closure to produce consistent kilometer-scale 3D reconstructions from monocular RGB sequences without retraining or extra supervision.
The paper presents a 5G terrestrial positioning system using multi-carrier carrier phase ranging, deep learning for NLOS identification, and IMU/camera sensor fusion via error-state EKF, achieving less than 5 meters error on simulated 5G signals over KITTI urban trajectories.
citing papers explorer
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Robust and Efficient Monocular 3D Gaussian SLAM for Kilometer-Scale Outdoor Scenes
KiloGS-SLAM is a monocular 3DGS SLAM system with condition-triggered hybrid tracking and probabilistic chunk-based Gaussian mapping that scales to over 10,000 frames in outdoor environments while maintaining accuracy and efficiency.
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VGGT-Long: Chunk it, Loop it, Align it -- Pushing VGGT's Limits on Kilometer-scale Long RGB Sequences
VGGT-Long extends VGGT with chunking, overlap alignment, and loop closure to produce consistent kilometer-scale 3D reconstructions from monocular RGB sequences without retraining or extra supervision.
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A Robust 5G Terrestrial Positioning System with Sensor Fusion in GNSS-denied Scenarios
The paper presents a 5G terrestrial positioning system using multi-carrier carrier phase ranging, deep learning for NLOS identification, and IMU/camera sensor fusion via error-state EKF, achieving less than 5 meters error on simulated 5G signals over KITTI urban trajectories.