From the 1 of 15 linked papers with an AI index.
16 papers
GSR: Geometric Methods for Fast and Memory-Efficient Gaussian-based Surface Reconstruction
Dasong Gao, Vivienne Sze, Sertac Karaman
The paper introduces G²SR, a method that detects 2D Gaussian splats in a few RGB images, then analytically triangulates them into metric‑scale 3D splats for fast, memory‑efficient…
Motion Planning in Compressed Representation Spaces
Lukas Lao Beyer, Sertac Karaman
Deep learning methods have vastly expanded the capabilities of motion planning in robotics applications, as learning priors from large-scale data has been shown to be essential in…
SCREP: Scene Coordinate Regression and Evidential Learning-based Perception-Aware Trajectory Generation
Juyeop Han, Lukas Lao Beyer, Guilherme V. Cavalheiro +1
Autonomous flight in GPS-denied indoor spaces requires trajectories that keep visual-localization error tightly bounded across varied missions. Map-based visual localization method…
Lost in Context: Addressing Context Anxiety in Large Language Models
Ifueko Igbinedion, Jillian Ross, Etienne Ricardez +2
Conventional wisdom suggests that reasoning models fail when problems exceed their capabilities. However, we find that frontier reasoning models sometimes possess the necessary cap…
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…
Flow Matching with Uncertainty Quantification and Guidance
Juyeop Han, Lukas Lao Beyer, Sertac Karaman
Despite the remarkable success of sampling-based generative models such as flow matching, they can still produce samples of inconsistent or degraded quality. To assess sample relia…