142 citations · 712 across the 76 of their papers we have counts for
89 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…
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…
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…
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…
Gleanmer: A 6 mW SoC for Real-Time 3D Gaussian Occupancy Mapping
Zih-Sing Fu, Peter Zhi Xuan Li, Sertac Karaman +1
High-fidelity 3D occupancy mapping is essential for many edge-based applications (such as AR/VR and autonomous navigation) but is limited by power constraints. We present Gleanmer,…
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…