RecGen achieves state-of-the-art 3D multi-object scene reconstruction from sparse RGB-D views by combining compositional synthetic scene generation with strong 3D shape priors, outperforming SAM3D by 30%+ in shape quality and pose accuracy while using 80% fewer meshes.
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2026 2verdicts
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EnDKF combines ensemble Kalman filtering with directional statistics and unit quaternions to achieve lower pose tracking error than raw measurements in synthetic constant-velocity tests and FoundationPose-based head tracking.
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Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations
RecGen achieves state-of-the-art 3D multi-object scene reconstruction from sparse RGB-D views by combining compositional synthetic scene generation with strong 3D shape priors, outperforming SAM3D by 30%+ in shape quality and pose accuracy while using 80% fewer meshes.
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Pose Tracking with a Foundation Pose Model and an Ensemble Directional Kalman Filter
EnDKF combines ensemble Kalman filtering with directional statistics and unit quaternions to achieve lower pose tracking error than raw measurements in synthetic constant-velocity tests and FoundationPose-based head tracking.