UfM* uses Gaussian mixtures to compute multiview disagreement for uncertainty in depth estimation with single inference per image, reducing energy and memory use.
Real-time large-scale dense rgb-d slam with volumetric fusion,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 3years
2026 3representative citing papers
A terrain-adaptive epsilon-constraint MPC with semi-parametric SGP vehicle-terrain model achieves 94% success rate, 24% lower orientation deviation, and 23% better trade-off quality than MPPI and GAKD baselines.
A surface extraction framework reduces the navigation state space by over 80% while achieving 100% planning success and sub-millisecond A* searches in Matterport3D and PCT scenes.
citing papers explorer
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UfM*: Uncertainty from Motion* for DNN Depth Estimation Using Gaussians
UfM* uses Gaussian mixtures to compute multiview disagreement for uncertainty in depth estimation with single inference per image, reducing energy and memory use.
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A Terrain-Adaptive epsilon-Constraint MPC for Uneven Terrain Kinodynamic Planning
A terrain-adaptive epsilon-constraint MPC with semi-parametric SGP vehicle-terrain model achieves 94% success rate, 24% lower orientation deviation, and 23% better trade-off quality than MPPI and GAKD baselines.
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Beyond Geometry: Efficient Topologically-Grounded Navigation in Complex 3D Environments
A surface extraction framework reduces the navigation state space by over 80% while achieving 100% planning success and sub-millisecond A* searches in Matterport3D and PCT scenes.