LeanGate is a lightweight feed-forward network that predicts geometric utility scores to skip over 90% of redundant frames in GFM-based monocular SLAM, reducing tracking FLOPs by 85% and achieving 5x speedup while maintaining accuracy.
Structure-from-motion revisited
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MM-GS combines per-instance multi-view fusion with scene-level interaction modeling on 3D Gaussians to render high-fidelity multi-human multi-object scenes from sparse views.
citing papers explorer
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Accelerating Transformer-Based Monocular SLAM via Geometric Utility Scoring
LeanGate is a lightweight feed-forward network that predicts geometric utility scores to skip over 90% of redundant frames in GFM-based monocular SLAM, reducing tracking FLOPs by 85% and achieving 5x speedup while maintaining accuracy.
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Rendering Multi-Human and Multi-Object with 3D Gaussian Splatting
MM-GS combines per-instance multi-view fusion with scene-level interaction modeling on 3D Gaussians to render high-fidelity multi-human multi-object scenes from sparse views.