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• HyVGGT-VO achieves approximately 5x processing speedup compared to existing VGGT-based methods.result 90%
• HyVGGT-VO reduces average trajectory error by 85% on the indoor EuRoC dataset.result 90%
• HyVGGT-VO reduces average trajectory error by 12% on the outdoor KITTI benchmark.result 90%
• HyVGGT-VO is the first work to tightly couple a traditional VO framework with VGGT, a state-of-the-art feed-forward model.technical 85%
• The system uses an adaptive hybrid tracking frontend that dynamically switches between traditional optical flow and VGGT tracking head.method 85%
• The system introduces a hierarchical optimization framework that jointly refines VO poses and the scale of VGGT predictions.method 85%
• Existing feed-forward models in dense visual SLAM systems are restricted to sparse pose outputs at keyframes and fail to achieve real-time pose estimation due to heavy computational burden.limitation 80%
• Traditional sparse methods lack the capability for dense reconstruction.limitation 85%

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