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cs.RO2026
UfM*: Uncertainty from Motion* for DNN Depth Estimation Using Gaussians
Soumya Sudhakar, Sertac Karaman, Vivienne Sze
Reliable uncertainty estimation is critical for deploying monocular depth deep neural networks (DNNs) in safety-critical robotic systems. Conventional uncertainty methods such as e…
cs.RO2024★ 1 cited
GEVO: Memory-Efficient Monocular Visual Odometry Using Gaussians
Dasong Gao, Peter Zhi Xuan Li, Vivienne Sze +1
Constructing a high-fidelity representation of the 3D scene using a monocular camera can enable a wide range of applications on mobile devices, such as micro-robots, smartphones, a…