1 citations · 2 across the 4 of their papers we have counts for
4 papers
GSR: Geometric Methods for Fast and Memory-Efficient Gaussian-based Surface Reconstruction
Dasong Gao, Vivienne Sze, Sertac Karaman
Few-view surface reconstruction recovers the visible surfaces of a scene from a few posed RGB images, providing the 3D models that robots need to explore and interact online. On mo…
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…
DecTrain: Deciding When to Train a Monocular Depth DNN Online
Zih-Sing Fu, Soumya Sudhakar, Sertac Karaman +1
Deep neural networks (DNNs) can deteriorate in accuracy when deployment data differs from training data. While performing online training at all timesteps can improve accuracy, it…
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…